{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5fbc2d16-59f9-4be3-b93e-1a5440c7efd0",
   "metadata": {},
   "source": [
    "# Tutorial 7 - Inverse PINN Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afe6a1e-3ab4-4f3f-ad47-f6cd66419504",
   "metadata": {},
   "source": [
    "We have already worked a lot with PINNs and in the last tutorial we saw how to define a custom KAN class. In this tutorial we will be combining these to define a PINN that solves the so-called inverse problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0a2ef2a6-f681-427f-8252-ade2111ce0e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "from jaxkan.layers.Spline import SplineLayer\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "\n",
    "from jaxkan.utils.PIKAN import sobol_sample, gradf\n",
    "\n",
    "from flax import nnx\n",
    "import optax\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60e70a5d-0340-4bdc-af4e-150d0098a87e",
   "metadata": {},
   "source": [
    "## Data Generation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "166ad90d-430e-45ca-86a8-e6e4dbc1c943",
   "metadata": {},
   "source": [
    "For the purposes of this example, we will be working with the Diffusion Equation,\n",
    "\n",
    "$$ \\frac{\\partial u}{\\partial t} -D \\frac{\\partial^2 u}{\\partial x^2} = 0,$$\n",
    "\n",
    "in the $\\Omega = [0,1]\\times [0,1]$ domain, subject to the boundary conditions\n",
    "\n",
    "$$ u\\left(t=0, x\\right) = \\sin\\left(\\pi x\\right), $$\n",
    "\n",
    "$$ u\\left(t, x=0\\right) = u\\left(t, x=1\\right) = 0. $$\n",
    "\n",
    "We have intentionally left $D$ undefined, as we intend to also estimate it (apart from solving the PDE), using \"experimental data\". The PDE's analytical solution is given by\n",
    "\n",
    "$$ u(t,x) = \\sin\\left(\\pi x\\right) \\cdot \\exp\\left(-D\\pi^2 t\\right), $$\n",
    "\n",
    "so it will be used to generate mock experimental data with gaussian noise for $D = 0.25$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b986e75a-6d4a-402f-bea7-36d13f4a7866",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 42\n",
    "\n",
    "# Generate Collocation points for PDE\n",
    "N = 2**12\n",
    "collocs = jnp.array(sobol_sample(np.array([0,0]), np.array([1,1]), N, seed)) # (4096, 2)\n",
    "\n",
    "# Generate Collocation points for BCs\n",
    "N = 2**6\n",
    "\n",
    "BC1_colloc = jnp.array(sobol_sample(np.array([0,0]), np.array([0,1]), N)) # (64, 2)\n",
    "BC1_data = jnp.sin(np.pi*BC1_colloc[:,1]).reshape(-1,1) # (64, 1)\n",
    "\n",
    "BC2_colloc = jnp.array(sobol_sample(np.array([0,0]), np.array([1,0]), N)) # (64, 2)\n",
    "BC2_data = jnp.zeros(BC2_colloc.shape[0]).reshape(-1,1) # (64, 1)\n",
    "\n",
    "BC3_colloc = jnp.array(sobol_sample(np.array([0,1]), np.array([1,1]), N)) # (64, 2)\n",
    "BC3_data = jnp.zeros(BC3_colloc.shape[0]).reshape(-1,1) # (64, 1)\n",
    "\n",
    "# Create lists for BCs\n",
    "bc_collocs = [BC1_colloc, BC2_colloc, BC3_colloc]\n",
    "bc_data = [BC1_data, BC2_data, BC3_data]\n",
    "\n",
    "# Generate experimental data for inverse problem\n",
    "def u(t,x,tau):\n",
    "    return jnp.sin(jnp.pi*x)*jnp.exp(-tau*(jnp.pi**2)*t)\n",
    "\n",
    "key = jax.random.PRNGKey(seed)\n",
    "idxs = jax.random.choice(key, jnp.arange(collocs.shape[0]), (1000,), replace=False)\n",
    "exp_collocs = collocs[idxs] # (1000, 2)\n",
    "\n",
    "u_vals = u(exp_collocs[:,0], exp_collocs[:,1], 0.25).reshape(-1,1)\n",
    "noise = u_vals.std()*jax.random.normal(key, shape=(1000,1))\n",
    "exp_data = u_vals + noise # (1000, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e9d9bba-71e2-4d5b-95b1-36167fb70c1a",
   "metadata": {},
   "source": [
    "## KAN Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd54c6c-3d28-4864-af38-3b22875d0f0a",
   "metadata": {},
   "source": [
    "Similar to the previous tutorial, we will define a KAN Class based on the Spline Layer, which will also include a trainable parameter, $\\tau$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80600c7a-09d3-4575-a586-b587bbdf6fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyKAN(nnx.Module):\n",
    "    \n",
    "    def __init__(self, layer_dims: List[int], k: int = 3, G: int = 5, add_bias: bool = True, seed: int = 42):\n",
    "        \n",
    "        self.layers = [\n",
    "                SplineLayer(\n",
    "                    n_in=layer_dims[i], \n",
    "                    n_out=layer_dims[i + 1],\n",
    "                    k=k,\n",
    "                    G=G,\n",
    "                    residual=nnx.silu,\n",
    "                    external_weights=True,\n",
    "                    add_bias=add_bias,\n",
    "                    seed=seed)\n",
    "                for i in range(len(layer_dims) - 1)\n",
    "            ]\n",
    "\n",
    "        # This is the parameter we need to identify to solve the inverse problem\n",
    "        # We initialize it at 1.0\n",
    "        self.tau = nnx.Param(jnp.array([1.0]))\n",
    "\n",
    "    \n",
    "    def __call__(self, x):\n",
    "\n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b350b56f-5daa-411f-9090-b544760c34ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a MyKAN model instance\n",
    "n_in = collocs.shape[1]\n",
    "n_out = 1\n",
    "n_hidden = 6\n",
    "\n",
    "layer_dims = [n_in, n_hidden, n_hidden, n_out]\n",
    "\n",
    "model = MyKAN(layer_dims = layer_dims, k = 3, G = 5, add_bias = True, seed = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f681d135-c885-4e59-87d7-1ea16a157c50",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca097309-be81-4959-86c9-2365faab1362",
   "metadata": {},
   "source": [
    "PINNs provide a unified framework for solving the forward and the inverse PDE problem. Nonetheless, we will need to incorporate the \"experimental\" data in the loss function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6723a412-c313-453c-aa88-9274687ee54e",
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_type = optax.adam(learning_rate=0.001)\n",
    "\n",
    "optimizer = nnx.Optimizer(model, opt_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f18ab849-3c05-418a-a30b-3928f77c332d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PDE Loss\n",
    "def pde_loss(model, collocs):\n",
    "    # Eq. parameter\n",
    "    tau = model.tau.value\n",
    "\n",
    "    def u(x):\n",
    "        y = model(x)\n",
    "        return y\n",
    "        \n",
    "    # Physics Loss Terms\n",
    "    u_t = gradf(u, 0, 1)\n",
    "    u_xx = gradf(u, 1, 2)\n",
    "    \n",
    "    # Residual\n",
    "    pde_res = u_t(collocs) - tau*u_xx(collocs)\n",
    "    \n",
    "    return pde_res\n",
    "\n",
    "# Define train loop\n",
    "@nnx.jit\n",
    "def train_step(model, optimizer, collocs, bc_collocs, bc_data, exp_collocs, exp_data):\n",
    "\n",
    "    def loss_fn(model):\n",
    "        pde_res = pde_loss(model, collocs)\n",
    "        total_loss = jnp.mean((pde_res)**2)\n",
    "\n",
    "        # Boundary losses\n",
    "        for idx, colloc in enumerate(bc_collocs):\n",
    "            # Residual = Model's prediction - Ground Truth\n",
    "            residual = model(colloc) - bc_data[idx]\n",
    "            # Loss\n",
    "            total_loss += jnp.mean(residual**2)\n",
    "\n",
    "        # Experimental data loss\n",
    "        exp_pred = model(exp_collocs)\n",
    "        total_loss += jnp.mean((exp_pred-exp_data)**2)\n",
    "\n",
    "        return total_loss\n",
    "    \n",
    "    loss, grads = nnx.value_and_grad(loss_fn)(model)\n",
    "    optimizer.update(grads)\n",
    "    \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b06282a5-8899-4801-9c9e-d8b73b55d74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize train_losses\n",
    "num_epochs = 5000\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    # Calculate the loss\n",
    "    loss = train_step(model, optimizer, collocs, bc_collocs, bc_data, exp_collocs, exp_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faab949e-dadb-4cec-bc13-63b10cb609c5",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef7d214-6efa-4124-a587-b47e98cac7d6",
   "metadata": {},
   "source": [
    "The following plot shows the trained neural network on the entire domain, approximating the solution, $u$, of the equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7798eb68-d6b5-47ec-b845-cf3d60dccf23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_t, N_x = 100, 256\n",
    "\n",
    "t = np.linspace(0.0, 1.0, N_t)\n",
    "x = np.linspace(0.0, 1.0, N_x)\n",
    "T, X = np.meshgrid(t, x, indexing='ij')\n",
    "coords = np.stack([T.flatten(), X.flatten()], axis=1)\n",
    "\n",
    "output = model(jnp.array(coords))\n",
    "resplot = np.array(output).reshape(N_t, N_x)\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.pcolormesh(T, X, resplot, shading='auto', cmap='Spectral_r')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title('Solution of Diffusion Equation')\n",
    "plt.xlabel('t')\n",
    "\n",
    "plt.ylabel('x')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba27f8f-c733-4114-824d-ace0c208f441",
   "metadata": {},
   "source": [
    "We can also visualize the difference between the analytical and the approximated solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "215ff5fe-6bf4-4636-8e3a-f27712af0274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r25K2sHbtWnv22Wft2GOPtQ0bNmS3+dlnn9nkyZPt3XffbTPkZGY2depUGzJkSPbf48aNs/Hjx9uTTz7ZYdvp37+/7bzzzvbCCy+Ymdn//d//WSwWs4suushWr15t7777rpmZvfjiizZx4sTsMarX5uGHH7ZMJmPHHnts3nxVVVU2cuRI5xqWlZXZt771rey/k8mkjRs3Lnv+t4bcfM7169fbmjVr7MADD7R//vOftn79+rx5Bw8ebP/v//2/7L9bQvRvvPGGVVdX58172mmn5RWEnHnmmRaPx7PXoQUjRoywyZMn50178sknbdy4cTZx4sS8YzzttNNsxYoV9vbbb5uZWTQatXvuucc2btxohx9+uN1+++02e/bsvHzurWHatGl5+zh+/HjzPM++853v5M03fvx4+/DDD625uTk7rT3njfDQQw/Z/vvvb7179867xpMmTbJ0Op29t5588kmLx+N25plnZpeNxWJ27rnnSseYSqWyBSfpdNo+++wzKysrs5122sl5js3afpZfffVV++STT+yMM87Im68l1UBBe87dqFGjsvtgtvnZ22mnnfLu7fYeYy6mTZtmq1atynumfvGLX1hxcbEdffTR0vHk4tNPP7UXXnjBvvOd79iXvvSlvL/lWjDlnoOmpib77LPPbMcdd7RevXq1uc+EXr162csvv2yrVq3Cv7dct7POOisvJ3bKlCm28847O+HzzoD6nLegrXszRIhchMVPAbBx40YbMGBAh63v3Xfftb///e/Wv39//Psnn3yS9+8RI0bk/fvTTz+1devW2Z133ml33nmntA7/ANy7d28zs2xO4fvvv29mZrvuumur+/3ee++Z53k2Z84cmzNnTqvbzSWdhJEjRzrTvvzlL9uDDz7YodvZf//9syTrxRdftLFjx9rYsWOtT58+9uKLL9rAgQPtz3/+s5144onZZdRr8+6775rneXgsZuZUAG+33XaO72Dv3r3tL3/5y1aPwWwzqZ47d64tXbrUNm3alPe39evX55GNHXfc0dnOl7/8ZTPbnIdWVVWVne7f97KyMhs0aJCTQ+e//8w259+NHz/emb7LLrtk/95yL+2www7Z3LZdd9211WtK8N+3Lcc6dOhQZ3omk7H169db3759zax9543w7rvv2l/+8pc274UPPvjABg0aZGVlZXl/32mnndo4us3IZDJ288032+23327Lly+3dDqd/VvLseSirWf5gw8+MDP3+iYSCdt+++2lfWrPufPvT8s+5eYrt/cYc/HVr37VBg0aZL/4xS/s0EMPtUwmY7/85S/tv/7rv6y8vFw6nly0kKStjXVmZnV1dTZv3jy7++677aOPPsrLV1c+bPy4/vrrbfr06TZ06FAbM2aMHXHEETZt2rTsNWm5bnTf7LzzzvbHP/6x3dsMivY852Zt35shQuQiJKYF4l//+petX7/edtxxxw5bZyaTsd12281uvPFG/Lv/peuvgG4pvPjWt75l06dPx3Xk2luZbVZwCLmDbVto2e6FF17oKGgt6Ijz1FHbmThxov34xz+2f/7zn/biiy/a/vvvb5FIxCZOnGgvvviiDR482DKZTJ7ao16bTCZjkUjEfvvb3+K59ZOUQs//+++/b4ceeqjtvPPOduONN9rQoUMtmUzak08+aTfddJNThLMt0BEV+C12UKtWrbLPPvssjyBvDa2dt7bOZ0ect0wmY1/96lft4osvxr+3EP6guPbaa23OnDn2ne98x66++mrr06ePRaNRmzlzJu5nRzzLW0N7z52yP+09Rv/6TzzxRPvxj39st99+u/3f//2frVq1Ki8CsS1w7rnn2t13320zZ860CRMmWGVlpUUiETv++OMLeu6OPfZY23///e2RRx6xp59+2ubPn2/XXXedPfzww3b44YcH2tfWzPZzPwA6A9v63gzxn4WQmBaIe++918ysVYJUCHbYYQf785//bIceemhB3Tv69+9v5eXllk6nbdKkSR22T2Zmb731VqvrbPmyTyQSgbbbEkbPxT/+8Y9sFW17trO189dCOJ955hn705/+lPWePeCAA+yOO+6wwYMHW2lpqY0ZMya7jHptdthhB/M8z0aMGNFhBIXwm9/8xhoaGuyxxx7LUyP8qQItaFGbc/f9H//4h5mZ4/P67rvv2sEHH5z998aNG+3jjz+2I444os39GjZsmC1btsyZ/s4772T/3oJFixbZM888Y9dcc43NmzfPTj/9dPvf//3fNrcRBO05b61d5x122ME2btzY5j04bNgwW7JkiW3cuDHvg4TOD+FXv/qVHXzwwfbTn/40b/q6deusX79+0jr8+2O2+fq2pKCYbQ5HL1++3PbYY4+tLt/ee05B0GOcNm2a3XDDDfab3/zGfvvb31r//v2dMVkdS1vGF3IC8e/z9OnT7YYbbshOq6+vD2RMP2jQIDvrrLPsrLPOsk8++cT22msvu+aaa+zwww/PXrdly5blXbeWabnPlB8tyqR/31pU2Fy0553Tnuc8RIj2IswxLQDPPvusXX311TZixAj75je/2WHrPfbYY+2jjz6yH//4x87f6urqrLa2dqvLx2IxO/roo+3Xv/41Dq6ffvppu/dpr732shEjRtiCBQucwa3la3fAgAF20EEH2Y9+9CP7+OOPC97uo48+mpcj+sorr9jLL7+cVQ3as53S0lIzcwdks80h6JamB01NTbbffvuZ2WbC+v7779uvfvUr22efffI8adVr89///d8Wi8XsyiuvdNQAz/Pss88+k85FW2hRIPxhxLvvvhvnX7VqlT3yyCPZf9fU1NjPf/5zGz16tKNS3nnnndbU1JT99x133GHNzc2SenPEEUfYK6+8YkuXLs1Oq62ttTvvvNOGDx+ezYlevny5XXTRRXb00UfbpZdeaj/84Q/tsccec+yEOhrtOW+lpaV4/xx77LG2dOlS+93vfuf8bd26ddl81iOOOMKam5vz7JTS6bTdeuut8r7676GHHnpIytcmjB071vr372+LFi2yxsbG7PR77rlHIlXtvecUBD3G3Xff3XbffXf7yU9+Yr/+9a/t+OOPd7ykS0tLpRB7//797YADDrC77rrLVq5cmfe33H2kfb711lsLUiHT6bSzbwMGDLDBgwdnbc7Gjh1rAwYMsEWLFuVZn/32t7+1v//97zZlypRW119RUWH9+vXL5j234Pbbb3fm3dqY6Yf6nIcIUQhCxbQN/Pa3v7V33nnHmpubbfXq1fbss8/aM888Y8OGDbPHHnusQw2aTzrpJHvwwQftjDPOsOeee872228/S6fT9s4779iDDz6Y9YzcGn7wgx/Yc889Z+PHj7dTTz3VRo0aZWvXrrXXX3/dfv/739vatWvbtU/RaNTuuOMOO+qoo2z06NE2Y8YMGzRokL3zzjv2t7/9LftyXrhwoU2cONF22203O/XUU2377be31atX29KlS+1f//pXm76EZpvD8BMnTrQzzzzTGhoabMGCBda3b9+8kKm6ndGjR1ssFrPrrrvO1q9fb6lUKuu/aLaZhN5///222267ZVWFvfbay0pLS+0f//hHXn6pmX5tdthhB/v+979vs2fPthUrVtjUqVOtvLzcli9fbo888oiddtppduGFF7brGhAOO+wwSyaTdtRRR9npp59uGzdutB//+Mc2YMAAJO1f/vKX7eSTT7Y//elPNnDgQLvrrrts9erVSCoaGxvt0EMPtWOPPdaWLVtmt99+u02cONG+/vWvt7lfl1xyif3yl7+0ww8/3M477zzr06eP/exnP7Ply5fbr3/9a4tGo9kipeLi4ixpO/300+3Xv/61nX/++TZp0iQbPHhw4HNEaM95GzNmjN1xxx32/e9/33bccUcbMGCAHXLIIXbRRRfZY489ZkceeaR9+9vftjFjxlhtba399a9/tV/96le2YsUK69evnx111FG233772SWXXGIrVqzIeseqeYhHHnmkXXXVVTZjxgzbd9997a9//av94he/kPNB/UgkEvb973/fTj/9dDvkkEPsuOOOs+XLl9vdd98trbO995yCjjjGadOmZZ8pCuOPGTPGHnjgAZs1a5btvffeVlZWZkcddRSu65ZbbrGJEyfaXnvtZaeddpqNGDHCVqxYYU888YS9+eab2X2+9957rbKy0kaNGmVLly613//+923mxBI2bNhg2223nR1zzDG2xx57WFlZmf3+97+3P/3pT1lFNpFI2HXXXWczZsywAw880E444QRbvXq13XzzzTZ8+PBsm+vWcMopp9gPfvADO+WUU2zs2LH2wgsvZKMl/vNkZva9733Pjj/+eEskEnbUUUdlCWsulOc8RIiC0Vnl/180tFjntPySyaRXVVXlffWrX/Vuvvlmr6amxlkmqF2U5222Brnuuuu8r3zlK14qlfJ69+7tjRkzxrvyyiu99evXZ+czs1YtRlavXu2dffbZ3tChQ71EIuFVVVV5hx56qHfnnXdm52mx8XjooYfylm3NXuSPf/yj99WvftUrLy/3SktLvd1339279dZb8+Z5//33vWnTpnlVVVVeIpHwhgwZ4h155JHer371K9xP/zbnz5/v3XDDDd7QoUO9VCrl7b///t6f//xnZ351Oz/+8Y+97bff3ovFYo5ty8KFCz0z884888y8ZSZNmuSZmbdkyRJnu+q18TzP+/Wvf+1NnDjRKy0t9UpLS72dd97ZO/vss71ly5Zl52nt3pg+fTra2/jx2GOPebvvvrtXVFSU9X+96667HMuXlnvtd7/7nbf77rt7qVTK23nnnZ1r33LP/+EPf/BOO+00r3fv3l5ZWZn3zW9+0/vss8/y5m3t/vW8zdfnmGOO8Xr16uUVFRV548aN8x5//PHs31usYnLtqzzP81auXOlVVFR4RxxxRN45Iruo1vbdbzfV8kx++umn7T5v1dXV3pQpU7zy8nLHTmnDhg3e7NmzvR133NFLJpNev379vH333df74Q9/mGdj9dlnn3knnXSSV1FR4VVWVnonnXSS98Ybb8h2Uf/zP//jDRo0yCsuLvb2228/b+nSpfI5ae1Zvv32270RI0Z4qVTKGzt2rPfCCy8462wN7b3n/PBvRz3Grdkeffzxx14sFvO+/OUv4z5v3LjRO/HEE71evXp5ZpZ9tlpb51tvveX9v//3/7L370477eTNmTMn+/fPP//cmzFjhtevXz+vrKzMmzx5svfOO+84lkqKXVRDQ4N30UUXeXvssUd2bN1jjz2ydna5eOCBB7w999zTS6VSXp8+fbxvfvObefZ6nsfvoE2bNnknn3yyV1lZ6ZWXl3vHHnts1jYv1y7K8zZboQ0ZMsSLRqN519R/bJ7X9nOeew7UezNECM/zvIjnhdnHIUL8J2P48OG266672uOPP77V+e655x6bMWOG/elPf5Jtm0KE6GqsWbPGBg0aZJdffnm7nB1ChAjRPRHq7SFChAgR4guLe+65x9LptJ100kldvSshQoToAIQ5piFChAgR4guHZ5991t5++2275pprbOrUqY67RIgQIb6YCIlpiBAhQoT4wuGqq66yl156yfbbbz/Z6SBEiBDdH12aY/rCCy/Y/Pnz7bXXXrOPP/7YHnnkEZs6depWl3n++edt1qxZ9re//c2GDh1ql112mX3729/ulP0NESJEiBAhQoQIse3QpTmmtbW1tscee9jChQul+ZcvX25Tpkyxgw8+2N58802bOXOmnXLKKegnGCJEiBAhQoQIEeKLhW5TlR+JRNpUTL/73e/aE088kWcef/zxx9u6devsqaee6oS9DBEiRIgQIUKECLGt8IXKMV26dKnTBnDy5Mk2c+ZMeR2ZTMZWrVpl5eXlBbX9DBEiRIgQIUJse3ieZxs2bLDBgwd3iWl/fX19Xpe09iCZTHZoA56ehC8UMa2urraBAwfmTRs4cKDV1NRYXV2dFRcXO8s0NDTktXH76KOPwnZpIUKECBEixBcEH374oW233Xadus36+nqrKq609VYYMa2qqrLly5eH5LQAfKGIaSGYN2+eXXnllc708Wf+zOKpkq0uG2vOONPiTe60TMxVXpP1zc60VF2TM60p6V6CSMbNrijelP9wpGPu12OiSevVHG+AY4iDeuzOhojC/mai7vpovsbimLt/cI4bU/nzqdtMNrjnpDnhnjtaNh3XvtCb4+4xeLA+uq5Nxe71z4CS77/HmpLuNukYMu5s1lQEj30Sls2401JF7n1NKO/lDuaJuHtdi4rd6xOH+YpL85+d4oR7LivgsODSWK+kuyzcElYEy1Yk3GmJqLu+ypT2LBbDsVYm4J71XYtkzF2O9oMAt4nFIu6BRSPuCWjK1DvT4LbGbdQ2uSfZf1xmZhua4cT7sBHW9Xm9u1wj7BsMCbYeuEd92t23tQ3ufGnYBrw6cD667/zDHz2HlSntWvdOudOqit1lB5W4Ozyy0r3W/YurnGlFDe6Y4H3wN2da0wt/d6Z9/NwmZ9qLv92y3TprtgvtJSsvL3fm29ZobGy09dZoP7R9rbidVKnOmu3C6pessbExJKYF4AtFTKuqqmz16tV501avXm0VFRWolpqZzZ4922bNmpX9d01NjQ0dOtSaBveyTJHbAzgXNCDEYFTzgJjWA7kq2eCOfirB8o+HRLhqYZRL0CgMUAl3lEZXAJE/2hcicA2wXWU/aH8zRHKBDBJpJMhkFY6flqXr3yDsn7ofVuHOF4MXbkmZ+9HUDC//IviQKCp2X0yxmDsYl5S689GyhIqS/HOCpDHpTiOy1gvmS4rEtCyhEcKKJHzUwnzFIsH0E9hYxJ0nHnV3OAL1rZTCFDX4uDJ3G565TIfKFDz4qi3NuM//pmZ3/8p8426T5+5vcZO7v6lGd10NaZoGH7BITN1pVgcfsDB00LI0jYgpfDc5qIR7mCLdfYHAFsPH1ZA+7gkoLXKPtaLEFXOQmPZy363pAe60+hJ3u31KtxzcJi9qtonv2c5CaTRhxZH2UaWoF5GFnRAuvlDEdMKECfbkk0/mTXvmmWdswoQJrS6TSqUslXIH0/jGZos3bXmgiJgQWSPCQSSR1FaVmCXr3AfdT6ZoXTit0V0XEV9CJENEyj2uFOyvSpyITJKKHGtWCLamXBbVuoNhGqQ1JNeN7n6o6nAzKaagfNK96CfiaXhyaZtNde58Bi+1+jp3haRcRmMigYeXP33oNQMxoe36iSM8mgZBCkvT/QXnib6FiHDUwf5Ggaw2wbESMSXQsv7P7jSQtYRIQgkZ0z5gmei68zUDCaXjovPun43uGzqX4rCG15XuJ/pWpWAD3Xd0XLR/tN0m37MThWMlklsCQy4dA5HhdQ3uONQ35R4YKeZFiTLYGZeERmg8LaIoypb5MpnNxLQrEY3p91Z2Gc8KIqYLFy60+fPnW3V1te2xxx5266232rhx41qd/6GHHrI5c+bYihUrbOTIkXbdddfZEUcckf37ww8/bIsWLbLXXnvN1q5da2+88YaNHj06+/cVK1bYiBEjcN0PPvigfeMb3zAzHkd++ctf2vHHH9/+gxTQpcR048aN9t5772X/vXz5cnvzzTetT58+9qUvfclmz55tH330kf385z83M7MzzjjDbrvtNrv44ovtO9/5jj377LP24IMP2hNPPNHubXu9o+YVbXkA6JVRn4EwK7xw65shDAYjAhHHZiBwcSC1fvJXVwqfvQAiUgRSIOkYiDTVl7pMh8haU8od/PCcwADm3xdaP5FQmo/OSQO8ceg6+FMKWtuuQi7NzDaRzAeI+naPCCJdaXqBFxW76mgcyBXd6xhmL3FfYElSDGEbpbAsEQf/tF4QoiyCc0JKaCmMeqSO0gucQu8ldE5gGimhNC0FKqo7j/bhR0ookdBUFBQuz71PMp67bNpzryGlAZQm3Puprtndhh/0IQFc1VL40UTKNX2suOezBnaNrhd9/BBxhGADI56/jUa4HUpg+Kd7ne5hIqt07ug5pPvJGZzMzDLuPRGBhyxV6p6o0rKc9WUyZmvd1XcmItGIRdup2Ebgw7EtPPDAAzZr1ixbtGiRjR8/3hYsWGCTJ0+2ZcuW2YABA5z5X3rpJTvhhBNs3rx5duSRR9rixYtt6tSp9vrrr9uuu+5qZpstOSdOnGjHHnusnXrqqc46hg4dah9//HHetDvvvNPmz59vhx9+eN70u+++2772ta9l/92rV692H6OKLiWmr776qh188MHZf7eE3KdPn2733HOPffzxx7Zy5crs30eMGGFPPPGEXXDBBXbzzTfbdtttZz/5yU9s8uTJ7d52ujZilvPizaihUVJb4LNXJXWlEN4nsupfNkFJTCKIIJHqSURSJpcwH21DVRv9+0zL0fopLK6SVVJ9SUWnc6KC7p005cD63sSkIiUhr5HUFlQpgTTStARMo5A/he1pXwj0Mi338XdSqYhwKiS3tWnap5+upqCKCi+8BN3bPrJKyiWRQQzHQ+idlDAVtF1CQ9rdBimLCR8xo3A/nXMxGwdBHyZECIlwlsbhfMJHnarU+iFkNplZK6SZVF/KO0fFmI6B5GFK7oYP7l5uniiNMQOqtjx5G5vNbKUzS6ciFtOvQXaZAu7FG2+80U499VSbMWOGmZktWrTInnjiCbvrrrvskksucea/+eab7Wtf+5pddNFFZmZ29dVX2zPPPGO33XabLVq0yMzMTjrpJDPbrIzifsZiVlWVnzf8yCOP2LHHHmtlZflKeK9evZx5txW6lJgedNBBOEi24J577sFl3njjjcDbLl3faPH6LYdPoXcKs9J8hEBpADCff320H0TC1PQBAoW8CU0pd7ulNdqyrI52XD4R5fViDi+cOzrHSJopbC/mkxJwPv80KFZSiR/NR+ooEU5aNgWEuBFCg/QSakoD+YVRXanro2IV9WVNoMtAxTr0UhdrUzBXlAmM78Mk4sp5qRgRU7iHRSJJpDZi2rNJRFclk/4cULqGdXBvqqD1UWShthnSgODUYe4ozJfW0qmd86R++ND9T1GEWtiP/phmABEoUNubMm5FWDwF4f0kRBZL3HE3mdqy3WR7GeE2QLQAxTT6b1JfU1OTN7211MLGxkZ77bXXbPbs2TnbjdqkSZNs6dKluI2lS5fm1c+YbbbPfPTRR9u1r7l47bXX7M0338SmR2effbadcsoptv3229sZZ5xhM2bM2Ga5v1+oHNOORP3ghMWLtnzVkQJFQAWqVgsho4oqhtD9aiARqeJa92WlhvyDhKMp5K0CK8mpQt5HYElp3OSX1UxXrtXCJGXfzNqhwANI+fTfnyWlGvFHJZTCzDAf7QeBii6oqInWRyonKaZ+wKVG5YqIBIVUKQyqh+PdZSk0SiSMCBEVWPmBuZ5AGiMQelWJaSQi5olDeD8Vc1MDaD6aFvWR7gyIF1Q0xvvmTqOKfrqug4rd80mkTi1qSsA9S/vnjwbg8wWXUL3/6Zkj0EcTXa94BA7M065PrLdbJJnMcepISLUF2xbRaIE5prY5VJ6LuXPn2hVXXOHMv2bNGkun02iH+c477+A2WrPPrK6ubt/O5uCnP/2p7bLLLrbvvvvmTb/qqqvskEMOsZKSEnv66aftrLPOso0bN9p5551X8La2hh5LTEOECBEiRIgQIbYVPvzwQ6uoqMj+m9TS7oK6ujpbvHixzZkzx/lb7rQ999zTamtrbf78+SEx7WgoOaZkDdUMoQVSPROg6FFomJQ6JV2AQvRq3mWh2zRjZVU9LlIqKRdVtaTyQ7XGopxQNRxP6iheC1ExxbC6kD+qKvyqOkqg8D6hpLTtAhaz1vZZq5BXhH8lX8+MvUg5zAxRD1CRaBpW1ovWUGRnVOQ7KaR6Uv5fDGxuSAlltVW0RvNc9V4N+VOqgR80HFCBWB1YQ9E1JDUbhklZJWOF393Guga4rhQx8F1aCseTKwOpr/QscZGYC9qG6m2bSLhKaIQsHSFUkcjxSk40BUgc7iBEY4WH8isqKvKIaWvo16+fxWIxtMNsLa+zNfvMQvNAf/WrX9mmTZts2rRpbc47fvx4u/rqq62hoWGbkO0eS0zL+zdZvHjLgIqWJPAC37TRfavRfJRjRzGZlLhsoy8khykFcAy0LvRiLRZDz+BYTcScvF2JrEXVNABfTiVY51kcEvvoPDXBOaH5KPQcaYbjh5Bfr1KtmITuO4WY0v4WQ/ic5iNTe9qmek5oPiycEquG6WXtD1OqpKEvjJkcegcnDHiB69M0Espm9/QybpuYqiF6ApFQyickwpmMuoSDKvoJSg5sWcIlvkTeifg3AqFAqy3Mz6axCT7q4RpSRX/fIi2f2D+NPnIobE+gFJVNkDvbB/aN4EGIXr7vwJIvAvUJyeItHndJ+LDubEQjnE6x1WXaudvJZNLGjBljS5YssalTp5rZ5vbpS5YssXPOOQeXmTBhgi1ZsiSvJXtb9plbw09/+lP7+te/bv37929z3jfffNN69+69zRTgHktMN22KWzyzhWSq5JIqmpGEAsjih9anKFWROu3OT4BtB1aRU7GlKkEBMHcWBnr0VKX8XDT08q1LLOAhFIFtEa2PphFZ21Sr5faS2kjr8xcYNcFxqSoq3XMEeibUAisVpPKgf6jvvKv5dJT/x/UUVPmtkUv6IKQ8VrVqnIlT/gop1w9VT1BHSUUlGyCaRtto8tziFzU/NS1UBBHxSUTF/Ge41nR/FVNlfaOmStI26F6k/FR0A/ARR7VjFAGr8mF/6R4m0H0nA4qfjHyxcyykGkS7w22JaCxi0XYmmbZXYTXb7Eo0ffp0Gzt2rI0bN84WLFhgtbW12Sr9adOm2ZAhQ2zevHlmZnb++efbgQceaDfccINNmTLF7r//fnv11VftzjvvzK5z7dq1tnLlSlu1apWZmS1btszMNqutucrqe++9Zy+88ILjE29m9pvf/MZWr15t++yzjxUVFdkzzzxj1157rV144YXtPkYVPZaYZmrM0jkf4h4pRhTeBXWMQOFdtcsPLesPl6sG9hR6LoIiKVxWDPmrFkpNUDVMx0r77Ce6CTKTR489dxK9XVQlHBVO8vsUSZ2qmNb69k81uqewPbwLZDQ2uuc4mdReHmgNRC0ZYXUVvhakRBA2wYufujwR1Haeahi0WVTlVKKjhLypij4GpA6LkFAd1SypiOhSWgEXSbmdhBoz+V0h2N5KPL+iDVIdqIh0rclTlar3iRCSeknTMr7tBilMV5fFtqpwnugeo1B+POVaQ3klbng/1s+9/tHY+pz/7vpQfiy6+deuZQrYznHHHWeffvqpXX755VZdXW2jR4+2p556KlvgtHLlSovmSLf77ruvLV682C677DK79NJLbeTIkfboo49mPUzNzB577LEssTWzrCG+vwjrrrvusu22284OO+wwZ78SiYQtXLjQLrjgAvM8z3bcccestdW2QsTbml/TfyBqamqssrLSJvzw1xYv3lI5SuSCHgo1vEkqKnbwgI47VNHsV7lICVTDsUHyEwl0rHRO1C4/ynGopJEQhEiqlep0fdQK+Rjcd8q1oPuVjO4JamtQ2gbZO6nVwBS2J2LW29enkQgtvYTLQaRRq+0JVDFPYfty6HdP+wduOWjBgpXPPhBpJOUSTdJFsFeqWiGvmfP7VVmah8gQdeUiwlXb7B5DDXxwqRX9NI2CTXUwdBAxJWXVD7VTGaWyULtcuq8HQiOOfkXufVgcd3MoS5qA1H76vjvt9b8502qf3mJcWtPYbNv99Hlbv369lKvZkWjhCr8aNMlKqInAVrAp02zHfPz7Ltnv/wQU7mkTIkSIECFChAgRIkQHoseG8utqExZLb5FTSsqg/R4oa2o+qaqYEig/0a+Y0bpomqpmYj6t+N1CSiAhSH6iX6nDYjVRfZW3KZ5jXFZU20lFTqXce9E5frFinvYjyLKkjnIBD6wPbifq4FSehMiC75ZVW4gqvQpaA4dyKeSrrY8LXUCph2U9X0FUkGp7FbIRPwTdaP+CGPb7wa4E2oWNi84KmGZC3sawXSowIih5p5Qn3V5fzVyo+aQN4nxY6BZxw/bYDYrC+xU5jW/c9OVORyTW/uKnbeQ732PQY4lpNOrlEYX6TdC6kkLK1D89CmEgsUKe3pLJYncbFPJXoIbUqdo+A92FVPcCtfOPGvL2z6dWh6vpDWraAp07yrFU10eV9A1w7qi7kh80eNJ+0AdHCsJ7QYousO0hHAIZ5RP56+Ujq9QukvaNjoFyDMneh1AB15r2l17+ZKavEgx/GBxzPSFEr4bZKX1AJY0qgaX5KLfVX/mNy0FaQEncJUgNYCFFrgcUyqbUANUuzO+iYMbPBIX3/R916gcXpcrQx5V6DPyciCASSuFwKIiK9d/i8hCr1+ohtiWiUWt/8dM22peegh5LTMsrGy0OhUe5UJXF8grXzqQWVM9SqMAm4kTERIFK8rDgplyrNkdVViyIoe1ScU5aUPQoD5MQRPUkEkpWSwQihI2N7vrKyrV7wj9NLTii619Roi1bGsDeiQud3POud6Fpe/3lCfjggG2WiB8NekGUZjVFiEXccULJMUV1lGQaT5uP9oNAZJWUUAIVYqXTEB3w7QspcqTIEhug/FS6Xp+DxyjdOzE4d3SfqDmg5KmrLEeuD+pHWHGcOnBp9lusesOJJ1cGVXbM/eqkL9BOxmZi2s5lts2u9Bj0WGK6YX3SYg1bMsMrerkxAyImNG1Djft1SITg87Vu2EIlTn5iIpMr0S6JyCWFcjFcDkSSFF7slQ77R+fYPV6taChIQRjP50ziwjmyvIJLoR2re95V9ZnDke40IojqOwH4oFwNrL7A/WoTFY2gETsoQeiBCXZBrHCK4eIOt9XyKaY0E5zLeNQdm1RyqdpP0aUmstKY2eRMI0LsV3lj5s6j2hapDQxIRaVl6bpyA4DCnQT8aQD0bFJ4X/3IIzVf/VgjBR5VeSKmNHiCYhrts+U9Ga0rvFCvoxCNRizaTmuEaIAUmhA9mJhao2eWMyjUfAJ91qFRhUpq0J9UzNkhUuffrprDSFD3g9RhlYQq/d5b2waeY0FFbdYKyxHydQXyx7mjmq1UUXHHXTNUWkU7IiKhRP5UeycmjnD9xYRPZTZ6d2xscu+vMqiYV3NRKbxbAesj8kNkhdRAqsD3E0Iiks0ZN3KTjoBiGAWLHjEcT4STjiECZEXtJOUnnaoiy/uBGbvOFCKcDaLVlNqwYRO4ATCtzwcRX/WDjqCSayLSaDVGHwn0FV5U5kyKJICY9toiGEUhnayzUZBdVI/yOup4hIpziBAhQoQIESJEiG6BHquYDt1pgyVycu2wa5AYZk2mKKyq+aKSAkfrU0zR6SOVlmtvvkz+smp+JuSsQoGN6m3qzCPmznZ0oZPcfpP6cddrBWEpCCv7FRIqpMiAcleipQ6iAkM9utUQOvmHUgqBaoDv3y55kZIiWQHXi+YjFYlUL7XfPYeBqZOS5jOaiLbd+k81xOewvbY+Cu8nom6KEqloUcgLzUAOqLKueMy9cfSqfyr1husPfSXL4L4mVZ68QilSQaqk+cYdUt/4fqUCJndZetZ5mjbWq0q4NbuKPrUp9XLyFDySrTsZ0VgYyu9s9Fhium5tkcXrtgyoSBDE4p8gJJQr2tsmRLx+rYoe81oxNcCZhPumFglxeoO7DYWsUgoAmcTL7WJFAivbLxGpg/spSC6mH/Ti2wRFrVjli8VK7kTVkodeiGrLUCoI8b906YVOZJXmo5c6DYRU0Z0ScxaJ1Ddn3IMtjoOtDsBPzpiEwrNOpvsqgQUrKxWYiwjro5xVfypDOuPexM2eS3KoIArng+rwBMxncD81ZLRKfbon6F4k8udfHz3XlCfKaQaFx5Rpu/ixonojUaU+5ZhW5oTyE11P8AoqfgpD+YHQY4npxo0JizdveVCI1JCfKOcJFt41B+cT8h3Vqmyqeler/tVcVLI8qt3o3lrFYj96xbqHFFnF/9VMt7JCsira7zRhQZR2/ZVuMGqxEpG8BKmtsCwpnKraQioqHT4RWPJ29OenkjpE+Z9EVvWPAco7hJc/zEf+pImoezEoL5J8QSM+lhATe9GT2kj5n0Qk1Q5RqI7SskB+iTgqICJNObZEpGg+Vc0kEspKJeUYQ8QIyWr+v9V8ZQI+h3Cs7ONKxwWCA5xPmhaH+98DYmq53aW019w2RTQaab9dVBCrrRA9l5h66UgeKcJCHxg0iISqhFM1gCdl1b9dqkAn4kMklMiqbBwvFgkRCVVBX6f+c0xpAWqlegnYdqE/qdh+lEChfNUDlIiTn2DSusgTlAgnKZcxSB+gYiValkD7R4SbQKTbr3LSS5NIqOpZqipLaiifiCNWL4OdE5Euqq73gzxBY2KYlQgchcaxSIqaBIgpBERgSSH1Q1V9iUiRYlrX7G6TiRkp4dq96A/Rt7YNf1SCfUfd1auFjjQfkXAmtWLTBRIXVAup3B3sBh3TIzEzyMLZ+jLbZld6DHosMR22Q40lcsiTEj43MyspcwlXPVhaqGbnqleonzgFsTxSu0GpHZ2I/BUXaaRO7RqkzMOVpe40IldFQDhoWQKtT62kpftEIXCq+X1pimxrYD7sWa/lmKo2NXStSUVS5lNVT7LBKYVcXAJV26sklMgPkSkld5RANktBKuuDmeRroFA+HofAApoy9dq6RGU1FSOjfyKE2tHWQN4pkVVSQ/337CYgtKTm0zOBaSsBvH1RWVfTQCiUrxRPdDFCxbTz0WOJ6YYNCUs0bxnIKJ8UfUwpT5Q8EEX1jqDYKqmkUSW+tD4KjVMKgaosqiSU8g79IBLGputtr8uMyZq6vxQaJwGOSB2F3xXbF8phVMPslKFBx0r7q14vuj6oIgHYezJ/hfQiVb1DSUUtBdNxDuW661NJMntAagU7fjJJL346uzpZ1ayBPFELotxRIvBcJBVrcx5aP6mvpA6qy5I5P6uSoJjCB1etqJj6708ioRTKV0P+tE2Vd7FdWACfviLwZCzLsTMT799tiWgBimlodxQM4fkLESJEiBAhQoQI0S3QYxXTpoaYeTmfQWp4e1Ote8qohSblcQbpKe+HWuG/aSOEt6hiWFSHcV9gWYKq3mLIW/iiJ9VTVSkx71JNFwDFENsD0nbFIib//qkqHZ0Ttfc2FSFhMZVoIUVV7qTe0D77Q/m0fhrMVBVV7W1P+0uqJxcdafZL6nyFrgvbedL6xAIrNZTLXaNgn33zRcTcVLUblKoik2yTMq1Yi3JRaQyT8ljhcqn5pDROqJEAQn3aHRTKIR2F8p0xlB+DlJfUlvkizV2fYxqLRCzWzlB+TCwcDsHoscQ0k4nkhczV4h8qJioVi2moAl3NC1XmQX9O8QoTQS6rcAdhJNJiiFYFkz9fVTakT1CuI61LnY8IEvmCogdogHGpd5IKIvL/raYyqC1J1aImOk9laI2kVSUXCr0Xfdt2PJunuctyyFM7UZTvqJBLM7bfccla2/O0b5sU3naXVQuYKARLqQwRoUiKnQsoX5XyeuEYRBssPReXiu7cudQCO/f+1K4hrX8jiAtlIGConqU0H907eI6B1OK03Ep9tS/yNkRBdlFhLDoQeiwxra+PWTzn8Knavmad+yVY0cs1Z96wwf0SLC8HCw3y54RG42vXuN6Gfl9QtaiJgMql6s8JUF0JqFKdCBG2x/QRUcprVJVQlZjR+uppHCXy507CXFQik7Rd/3xo5SQeK5FV2mYx5c6KnoqqTQ29TLnPuLusgiQVEoqV+qQspajKW+j3bmYWhfkIaCgPbUqd9QNBRLKqKpdo/g8f16COsdWU1s7UT0zV80sFUXis4ISgEngC3des1MM+472YP40/kDS7NLWZBIFysSlPWD53oI6iitqc5v/uIhRksB8WPwVCjyWmAwZuskROjjUpl736UIcQF0QSqSpdjQbUgUNAMXSD8oO+0lV1kAzjeFnqcuTOh16cwNZ6QdV4vWBYTqShFC2P3G2qFfMEVVkkqHZRBKVSn9ZP+0svNXoJERkktUUll0GKM/zbxcp96NRDIOWWjotAtk1EwmLg2UgvcCJdhQJVz44uHqE0G08ssKIGAESm/YVeVDQmKndEVukaepBngykKMF8iSv3jnUk8xgDvyvieCUoLoPGvl1iESvtWDg4UelGflt6CuUykrOYqpmAL2NkIFdPOR48lptGol0coKT+zZp07gFG70NbW70yDlzCFpBUFUu2sUwdjlZo7mQYiqVZq14CyWArvYCKhBP9ATMSEjqEWjPiJwAYhjbLqKV4zJXqlhvI5X9OdRmS1BC2k3GmqAlMqkj/aF7+nahxIKHXlIcsnAqqjMa2KXA1l0lmidp4EfztP7ugkRjhQWSX7BrpeNJ9Yla0SZ/+5w2p+d3BSc0zVDwRUYDFnl2yqtIp+RVklVRVbmdKaxG1i6g35XWNb2badFczMLFniTgNbtTyD/eauJ6aRqGeRdrZyau/8IfIR8voQIUKECBEiRIgQ3QI9VjGtq49bc87XH/mYUr93AnUSotQA7vLkrk81p/dDyU0008PR9LVNxT+ECrizKJTftwhyZYWe0tSViKCqo3SsBAzRwfmk6nXFn9SMUyj815GOgfJu1WsdpK2oCnkbYi6qu5yWi8ctJN31kcE6hvJBHVJD9HI/et85wY5GoGbh+tXwvliVL89HoP3zxUHpSSfVV+1URXmtJGeraRC0viTcxDEw598IRvx+RZPaG1MkgCIGHQ0sYBPzk/FCJiBikHv9u0FMPBJt/y0e5JEI0YOJabo5apGcZHGyd9pQ475cSqHzU2MjdX6CQVKc1tDYdo6pSjjI8kfNOw3SQpOKhKhlpkJCCVTUohIfmk9tl9nRIf9Cz7FKQomYEdSuNGplMQG70MB8yjaoP72cww1FImWQKI0tREUQWVHJKhUT+QkxEU4iUliERGSQWj/SfBS2D/IWpuKXbYwgna/ITJ4K0zIR9xqm025BbKHPk9rvPk6ZF+L6CFyIRjmmovNBwjXY725B8EjEs4g4juYuE6Jw9FhiqoDIKqVdUfU6dUiqr3NPd2WlO1gRMfUTEyKcvWCMD6KOElnDtDOYT1VW1e0qifhMzNxpaoV3kC4/6nx0rETg/CRRJaGkDqoWSmXgGEG5bWorRNUrlODvpKMWcHELUbJBgpxQ8qKEFjBoXQTKKimaVCQVN/CF9CmknGMaQOEE0iQvq6q+NHgo68PcVPBdFr1TCWgrBfcEkdA0eHBQ21MCfRD6LZ64i5R7/an4jyJLtL4g3csiQCNUn1m8Oqmc+7+x6wleqJh2PnosMa2vy7eLItJIFlLFMI2IKRnxU+HUBjDAJ9unVDI/EZ/M72upIASKf5qADapFOGQIT+qoCrXPvH9aEIN5JKbiQKKG49X9w/UBgVOUVWw/ivsL9k6iOqp6gKpkVSXJDb5zkoLzQcsRkU5jn3H3piiKQbtEAFfgQ0EUKUtYJCWES7FnPYT3YYjHNqhUhILKqkguCVBMJq2P1o8tT0HNE5VlKnQiqEogbSMJOT+1TaSst71+lYQS6qDRRZnoRY1tauF5avZcYp40eJ5IMc8N78PHcWcj4iuUVpcpBAsXLrT58+dbdXW17bHHHnbrrbfauHHjWp3/oYcesjlz5tiKFSts5MiRdt1119kRRxyR/fvDDz9sixYtstdee83Wrl1rb7zxho0ePTpvHQcddJD94Q9/yJt2+umn26JFi7L/XrlypZ155pn23HPPWVlZmU2fPt3mzZtn8fi2oZA9lpgO3q7WEiVbbp7evV1rKDUMTP3YSdHsLUet3MHKH2lMlLkDSTGoY7Qf1Gc9SHg/iIoYJDTuRwnkk5KKpuaTEpGi9VWAOq5Ww5KiSaFmP0kkMqhX/bpQiSSBCGwcyV/HVe/TuujFXATeiZQnSuDcUU0dUsmqbtjfdlU+KpJpzdsTjc5VJVSdTyW//v0j/0tSqWMUUtfyHyPwAFDYPgPTGjN1zjSy0KL1lSbc+8SvttLHFY9NziQcrzq6wYTa+Qs/iJpBWc5dVr23tiEikQIU0wJsTB944AGbNWuWLVq0yMaPH28LFiywyZMn27Jly2zAgAHO/C+99JKdcMIJNm/ePDvyyCNt8eLFNnXqVHv99ddt1113NTOz2tpamzhxoh177LF26qmntrrtU0891a666qrsv0tKtjgopNNpmzJlilVVVdlLL71kH3/8sU2bNs0SiYRde+217T9QAaHgHCJEiBAhQoQI0YW48cYb7dRTT7UZM2bYqFGjbNGiRVZSUmJ33XUXzn/zzTfb1772Nbvoootsl112sauvvtr22msvu+2227LznHTSSXb55ZfbpEmTtrrtkpISq6qqyv4qKiqyf3v66aft7bfftvvuu89Gjx5thx9+uF199dW2cOFCa2wUU4DaiR6rmPp9TNevd7+2qSUne2W60yh3sAaKQUmpIvWS+rEr6yqHUAh9WavqsNpxKEjRkRIu58pqTR1VlUDehrasmmOpbtevcgTJ16TrQKbzatckUkfZdJ/Cqu6+FHpspI6SP6ca3lV9QdFMHKC2x+xQU3xV6lG9SLGKnpq5w/rUbfjRBGF2quYG0LlUvWgjYmictkGqfBpUacpj9oMq/CmlgCItsmcpPJuoLFOKCt3DkIuNXrl07+Rei26QrNkZPqaNjY322muv2ezZs7PTotGoTZo0yZYuXYrLLF261GbNmpU3bfLkyfboo4+2a9tmZr/4xS/svvvus6qqKjvqqKNszpw5WdV06dKltttuu9nAgQPztnPmmWfa3/72N9tzzz3bvb220GOJaSyeyTPVp5xNmkbm9zGx1aaan0jz+SvaqSpbzzHUtkkIYj8UxOLIT9Zof6mQgI5fDZ9TuJiOS618p5cEpR9Q6M5PumldRAaJrJeKpvOUi0aFE3oPcGm2VlIe2n5ByZ2FxJcdhh7xZS3a5QAo5K/sCxEaPC71xU65fmp4n7YBYXW5yt8/H61L3jeXwNI90eS5qVzYSYsKfSjvEu6dJOQso52Xb1JTxlU0sPMZPJvcOEIbJ9vZhTMPshE/LdztiGnhxU81NTV501OplKVSbnHjmjVrLJ1O55E/M7OBAwfaO++8g9uorq7G+aurq9u1ryeeeKINGzbMBg8ebH/5y1/su9/9ri1btswefvjhrW6n5W/bAj2WmDY1xsziWwYo9BilL1DqdwxE59M6ahnnzkcFRuSB6VcqKZ1MzdckMkhQSSiRRNV+SW0Z6p/GJMxdjqvytcGae7ZrozURzk1QiEbHyi+Otrer5pPRfFRMEaX9gO2qxFz9SFCVmkLmMSs8r7O1ZYmYNmc0okN924ms+BU4VHNVlZJIHb15aVkkiRDOU6v3STFztim+ptRjgPmo2p4KoijHFHeFPpIg77QZCtb89xiN60RWCfR8+av+zbglL6m++BEG55Ps0uLW+dZgHYEgLUmHDh2aN33u3Ll2xRVXdMyOdRBOO+207H/vtttuNmjQIDv00EPt/ffftx122KFL9qnHEtNMJpJHRonoUaV+377uYEUEjgqMaIAhWyVan/+lDjnzsjpKxFetrFa/oulYK6HvcQOocko1uBoC5labmmcnqxKaUsnkVyt+YoLd9vGTwovKqlg4RARZVWWZcGskVCGOKkEkRVJtyUnFKrxsxxY/RRRCTOSSoPYnR+WOpkFYvaMLVPxEh0L5RGhJ9SWyCiwjDfZOeO8QuYQKdPqCS5t7zVhtz59G92YSPhAa4QOBxjUioXRvksJJZJU+pFgdheeaPjpyr2O8sGYzHYkgPqYffvhhXr4mqaVmZv369bNYLGarV6/Om7569WqrqqrCZaqqqto1v4rx48ebmdl7771nO+ywg1VVVdkrr7zibKdlH7YFeiwxra+LWTpnsC8phWrLBvdhJXuntNj5yZLazU35pH61FW1wxHAMqbSqd6ga8iesB39WyoGlY/MTXfbs1HIY1WmqAqumFRBxVrfhJ51ImgNYyBBZp+tAOca0LAGVUKqaFiqpVeWG5iNbJSIciYj7EqFtUNg2rprpi33GHR9TImakXKLUI1bHqzIREGmZrCoxUlUJbaRkf+jPDio1d4iiqvzCiRJWrwODVbbBnb8gHQlTytQUJe3jD1MeaFnKp6UPrNxrW2hecgciSCi/oqIij5i2hmQyaWPGjLElS5bY1KlTzcwsk8nYkiVL7JxzzsFlJkyYYEuWLLGZM2dmpz3zzDM2YcKE9u2sD2+++aaZmQ0aNCi7nWuuucY++eSTrDvAM888YxUVFTZq1KhA22oNPZaYhggRIkSIECFCdAfMmjXLpk+fbmPHjrVx48bZggULrLa21mbMmGFmZtOmTbMhQ4bYvHnzzMzs/PPPtwMPPNBuuOEGmzJlit1///326quv2p133pld59q1a23lypW2atUqMzNbtmyZmVm2+v7999+3xYsX2xFHHGF9+/a1v/zlL3bBBRfYAQccYLvvvruZmR122GE2atQoO+mkk+z666+36upqu+yyy+zss89uVQEOih5LTOPxjMVzlJ5myP8rKoZQBqhDmAMEKopaEEUm9n7lS+0BT6BtkhOACtouhW1VkNqoKIZqrqPa5Uj1+yMllMLqJKKr/oF+ZZUq4SlsT8b5dG1wWTidpI6qRU2KEtoa/PmepNKoahaFFDmkLpq4BzBdx9QAKX8UlCRSUSkM3hkqVJDCKUWeohAwVeqLXa6iEBon43hU4OG+o5akqors3CcYVQH1FfYXmzWIzwmrsoW/KJrIn5tU+dz7WMlB3sbojKp8M7PjjjvOPv30U7v88suturraRo8ebU899VS20GjlypUWzTlf++67ry1evNguu+wyu/TSS23kyJH26KOPZj1Mzcwee+yxLLE1Mzv++OPNbEuuazKZtN///vdZEjx06FA7+uij7bLLLssuE4vF7PHHH7czzzzTJkyYYKWlpTZ9+vQ839OORsRT/Cr+g1BTU2OVlZU25e5fWCLHRJZC+VSBX17mPlzUQrSsyB0QMLdTDKH7p/WCtAAiiGreqUqaKeSvVNG3hkIr6ZW2nWZ8TuSCIDE1gCrV6RWk2iApVI07JgkLmn5c7ErQsQVGqtm9H2pIkV6kamU9IRF1yQ+G8uG4MGcPim4w5OknNURyiAyqrUbJ6JwQJESvFj8VSkwJtH4adOPudVVzQqn9KKWLpKFgidIF/GSS5iGobVAJbKumOQtgq134gEvFSt35muH4a7bkTdZsqLNeI86y9evXSyHxjkQLV3jnvydZORV1bAUbmpps54d/3yX7/Z+Arv8c6SI0NUXNcqoTiYRSv/u6eveF27tU+wLFanNYNAnPgF8hVUkoV3S601R7J/bA1NRGgkqcFdKlFhepHYhUokfrS4jKKhUi0T777x21AxXZYHFRV+G2LHqVu9YyUslZw0InWD9VByegqIX2Ixlz8xOp2p7IKu1fIuqGvdBTEzwgLS0QQrECHUloR9vyqCRUIbpEQomEt7d0Om99UPwEKioWOgHwXoe2t0Rg/V7JRPyCFCYRsJ0tEk7tQ4+OnyMB8PwntpDfCEQGOxubq/Lbtx9BbsUQPZiY+rGxxn2Q4v4+oGZWVu6+6NbXgd9jShsQ1AKjOp8aWAeElsL7atheLYjSe9trIWS1GtxPYKkwh6pNuSpV8zFVTefpGPi8F1697CeipDSrx0DElH0xyamA2g8S+XdvMiKJ6gvMv74msmMSX5BqwRGR0DiQS1K01FSDKNw76J/qXx+RULUgSiV6QartVcVUCeUTkUZTf9FDj46VKvoBaJwP15/US7quyv1Jy9E09b6m5xCN82EbdKxUJEjboDASZih2t5akAYqfQhSGHktME4mMJXKIDH0RJcHeKA7kh5YNYqtEVlP+9ZWLdlG0fppWKj5IpISyvZO7LJGkQhVY9ux018U5ke76eyXdAbch454UtXpdNbtW1+cn3XSs1CGGEgOC5HrSfHEgCWrYWg15+6clo2BWDm8+tbc3hVljQEJlmyqsVHcnGRCYCHmF+vcPrZzE3NEA+Z9e2iXrKshWSyawyjy0roZN7jQ1DUCE4jtrZkjMyCvVT1bVHGYVaNsECOJykQDCjc8EOIl46aac/+4GVflWgF2U2A0uBKPHEtN4Ir/zE+ZgU2hYDC2Q2ki8gQqdlGVVEqoa3atFONQ1SE0rIBURcyxhECCF0N0m5HDCuqitJn1c9EmREkKkEfLJYBtEQilnk4zt3VC2RjhJzVRD70FacpIHpFo4oahItG9xyv+kdonwYqYwKxrsw7Jiz4V2+IeKpvjKugLkjnpNddo2xAIVLMQh8usn4XDskThobVT8hB6ooopKxwofDSqBo2n0geUnq9QGGe9h0XpKzYkmqO13CTjuwLMTiSVy/ltrJLAtESqmnY/w9IUIESJEiBAhQoToFuixiunGDUmLN2/5Skyl3C/LEjLOFw3R1ZxNTIESPkrVwhy15SkBc10hj7FBDKH3hnNMnZ8olO+fj0LlpI5iYVIBVh4tUC2kuPLfnY9UTtUA3JkH1TxSTEgd1G6KIMb2BLVgya/oyN1mRBuoVNStGFaroTE0imF7sTiJoIS3A6h+NB8K3KRwqsoq5HE66qhByJ8qotVOVWouKj1eYs5qjNrKinmhyvNEKTCYw0kNVsRCP/nZQSUYjp8UeNrBthT4HmQXFWILuv6qdxEiUS8vfEuh3GYgYbWb3AeztIQ6hLjbJEKoVogrfespX5MKnSh8TstyByZ3ICkW0xsovE37QgTTP/ZRSJ1smwhEGtmVQCOXKuJQbY1tKoHp+udT885Uwokhb3gxceWvli6gFl0o+6eSYTWfDguYxHBskPAmQu157wflTtI0Cm8DPPK7TAMhInJN2xXdADyfTROF7T3YD7onZGKjFpPRMZAHKjyfStjezH12KP+ZPUa1jzyCnjutjSdR+KpBh4BuQDzbQiTa/ir7MJQfDN3/rthGSCXTlshR8Jqa3DuJzPSLiyif0F1/LxAl6Oauh6pxKn7ybwPbYIp+orQskVCyH2LFUCN6ai/3RtgXf0W7akdFyi0RZCKcNI3OU3GcSBK9cLU8RnzBOuvXllN9F9We8mpRE1bvQg4oIUrengKBVdVcVYHC1qDUzpTIbzOQP1IqG6E4R4Fqpk/KFZFLNddVJRLgT1kogUVFVlZfO1CRbm0+OE9ROC61AYTTfheOIREB31WyRhPnU8erIIVYGPVpKryYrrMQKqadjx5LTDOZSF4/+/IK92Gtr3NPDxFTgmKSb8YklEitf1nubexOY9XT3SYV8JDVkqqOUrGSWoikKJ+sZmqV+jEIn6exspYUQ3d9RHSIwMThJaEWIimKIS4XoNCBq/fFDkkwtKjHqqicQdIH6LhiBrZVmGYBKyQS2tHWTf5psoWS6vcZQPUkiB2XCrYDIv9LVFbBViwGhVMUtjc6x9rHFd0nqHLCB6FrF6WF3uXoA+xcFAsn3WXV5hQZTIOB8042XbkfWN1AegyLnzofXU5MFy5caPPnz7fq6mrbY4897NZbb7Vx48a1Ov+CBQvsjjvusJUrV1q/fv3smGOOsXnz5llRkThg/BtFxWlL5LQczZCVD+SYEtHpC88bjf0V4J+phOg3L9v2fhBZo1xMAhHJYrg7yCsUVUTRLonmI5Lsh9qBiCxqMESNHphqtTEoxhSih+2qhtX+sT+IJyiBXpCkrFI4EsOF5DYgklolrKgajCOI1KgECUO5ZAwMSqhqdq/kopIS2gzKIpFcWr+as0mg4w/S9tS/rGgphcoqVv2782GVP70e0aaL7glIjYlpqTz+R5bzNbXUG1ZRNS9edTxRW/ciuoFPaVuIRAqwi2rn/CHy0aW8/oEHHrBZs2bZ3Llz7fXXX7c99tjDJk+ebJ988gnOv3jxYrvkkkts7ty59ve//91++tOf2gMPPGCXXnppJ+95iBAhQoQIESJEiI5GlyqmN954o5166qk2Y8YMMzNbtGiRPfHEE3bXXXfZJZdc4sz/0ksv2X777WcnnniimZkNHz7cTjjhBHv55Zfbve1MJv/jt1cfN+SjGudDgyi5oxOBCpacbYr+n2xW764vyP5S7miQFALeRv43FHYgAhWBwuwEzCcMoMqp2+WwGnUhyldlsMe62G1IbV1I6qhqWI9KKOWsQsoDQU0DcKCGntWcTQLNRypfABXRH5JGs3pcUDSiJ9BspBiq/qnkM4opCb4Nq24DYv4rqqOqckfpCHQ+IUStdk1TOj9hy1OxCC/ImCCn4+D4TLYB1Iigmf+7ixAtoPgpbEkaDF1GTBsbG+21116z2bNnZ6dFo1GbNGmSLV26FJfZd9997b777rNXXnnFxo0bZ//85z/tySeftJNOOqnd208mM5bI6eyUgXzKEgi9k+UTETgK2xOog5OCIB2YqKhH7VREoDadRFbVtAKCP9+P8wS1l4Gan0ndS4JYLfELprA+2Gp1OHdI0siwep7QagbmiwBxiEBeMNvP5O9zNA2EFn27xP7sqhE7gW5rlazR/mF+Zv4LmhoC5BqTb5lP7KxE+0HpCGpOLOUOqmkQagqBsv5kiTPJa4a8U5Ws0q4l2siTzK6PigfabkShfjRnxAp8bhfsXkNMF4CPS6rAp/2j8RTzs3M/YCB1rLMRFj91PrqMmK5Zs8bS6bQNHDgwb/rAgQPtnXfewWVOPPFEW7NmjU2cONE8z7Pm5mY744wzthrKb2hosIaGLQNRTU2Nmf3bLipHTaQcU0IJPFuUJ1rb7K6vb0qzH6L3q796nbsyaf3Tudre3WYvmI9Ax0A5q6zeanlXfiKq5itiZxl44VDFOPppglJJ+ZkEOi61Gtx/HHQMqZjrxan6CdLLSlVg4pjbRhZqcN+h/xbcd/7rKHpxyrmeBJpPVVFFsuY11sJmC/taRXunIN2mUPUlD1CxSKpQ9RbVTLKGKlymku2n1KIuUofpwwwtyXx2UQakkXxnsTBJ67alVu/TmECFTkhW4bnGWoHcZ6c7tCQNi586HV1e/NQePP/883bttdfa7bffbuPHj7f33nvPzj//fLv66qttzpw5uMy8efPsyiuvdKY3NcbM4lsGgKRIwijkXQyES+1RrxY/KabwREJ7gak92jaJCix7oKqm81T8o6mSfuKoGj2jsipUwtI2W9uu6pWJFfJAdJUwWExuA6kB/VSpKl3cCIb8VPKjhFVpXaJPJgK93DZq2yUSgl6k2q4glJBms6iE0vklAiBX1osqqkocaRt+qNdVVX3pA5ZOSVI81ggVtWmk3vOpqGrBETZJQPeKAJEQ9DZ21Wa16BKRe590h5h4NMIv/raWCVEwuoyY9uvXz2KxmK1evTpv+urVq62qqgqXmTNnjp100kl2yimnmJnZbrvtZrW1tXbaaafZ9773PYvCTTx79mybNWtW9t81NTU2dOhQi/oM9lNJ96Ee4EZB0d6pTvQiVfvWk7Lor4an+55USiKXVAlPqQFEQpOwYe4uoqqjmiWRkrOpeoLSgEsh7yA5mwSufNUeQT/5VbvI0CQkjdipKIC3I0HtAa8oa+o2VeKrkkYxRC91NGptWWhi4RwHkXAKn9M5V1VfFepHQlIk8P57Ao3zA1x/FWqjAzrv6vqAmEYy+WMMpcB4VPVNH/5gg0am/gQ1n1xelj7g27rXu4H0GIlGLNJOotne+UPko8uuejKZtDFjxtiSJUuy0zKZjC1ZssQmTJiAy2zatMkhn7F/W3AQETIzS6VSVlFRkfcLESJEiBAhQoQI0f3QpaH8WbNm2fTp023s2LE2btw4W7BggdXW1mar9KdNm2ZDhgyxefPmmZnZUUcdZTfeeKPtueee2VD+nDlz7KijjsoSVBWpomZLFm/5glV9QalTU/8iTR2lUD7le1LRkX//qJCI2nT2SlIHnsKr6LnVHKUBQHhHrNSmUL5fDVTVTMphko3YYT5SR9XiBPQPhPwxrBHzbUI9BrmPu5qLSNjWPeAJatW7GramXFxV9RRBRTd6bqdvPgpxqoq0rCKL14b2hdRRtfjLryKqTgAdfc9Rq1HwSiVFEyUftWuWP59cjI5wQZTWqYzzPwv3RZbRVtOF7uBzGotu/rV3mRAFo0uJ6XHHHWeffvqpXX755VZdXW2jR4+2p556KlsQtXLlyjyF9LLLLrNIJGKXXXaZffTRR9a/f3876qij7Jprrmn3tpOptCUh/zIXn9YB0YPizXXwbA2AcZnbg2rdivw1Ig1p98Yn6yXqhkQdnYjo0nxFMcrFdE9Kswfm7BHIjQAo4SLM9cSUArBBirmVumq7QLVNJxNuyM9Ni3mB/pezSi7UaUHsjdQCBbWXuRIuR2IWgFxjfh5cQ6zohvWphLAtu5zsNN/+qWF7QqHpE61BJZwFEjM5riend4hOCOJHAzU2iCTdQkSE8OygmwWOf1prYExHEse1IB3XkGC3VWBXqENDRyIaaX/OaBjKD4SI11oM/D8UNTU1VllZaQcvfNDixVsISnGx+wBX9HIH3CKYr6pE7NsOxbYDRLW1b1H+A0rFzNSVifJEaT4C5aJSHiuB1FbVVomIrp+sqp6AtC610IkGUtUuSm+PWdiXtayYqvmkQRTTIES30KpbdX9VgqgSXTgGUlaxuxTZOZGKqpw7xf/TTCehBFkxp48rscsVFY4p2y2U5JrxvtEHgtpdLgEf3FRgRVCq98V1NWXce0nNYacub3LbY9HvFHPx6Z7IyYGuqdlklVUn2vr16zs9Da+FK1T/zySrSLXPKaOmocmqbvh9l+z3fwK+UFX5HYlEIm2JnDB6San2gkxBr/h6ENv6wHhDfp9kpt8E47JfvVTJZQkQSbR3EkkoLVsEFYsU8lctidDiyTcfVZYm4OXC1eaF3/Yquab5OFxGiqGwATVsTUUSAczJZQ9Q1WpHJVh+qISWiCRZAxFpJBVdJZzwoqcXuGzd5EcQxRgVeaqiD2AD1SyuT7l38F4K8CEFERPcDyKNBFWpLjTVQCT+VCBKH9IElYRi2pI43xcWoWLa6eixxLS4pNkSJVsGFFJCKdTfGz76iuAslgKBVR0nKpPuspSL6gelCqjm99wNyp2vSDCENmvFGknMzyQy6V+W80lV0ujuL4Xe5fxPhOYVGigHUFmXqtw0gDVSkPBukLxTlfz4QaoXQbWgEfvYe+qxKjZIrS3r3xd5m2LFPCGIYhok1cC/XZW8q0RStRUjskr2TnRYkPKE+ckgmDvnjooTgPhHKCcWVq/a1hHI25SAHsiqet/tckwLsItq7/wh8tBjiWk87lk8hzwSCSU1k8Lx5GNKBJaLn8gove2QPCmhBLXlJ5FaCm8TuVS7EKmenUqRVCLm7pteSEUKjDsJ3i26TY9KQuilri7rB71cycdRXTaI72gAAqsomjSPkcJJL0M4v9ghSe3KRFAtmQp98aqdmlQlXAWmNwAJaQTGpbah88/XDNc1CQNxkNaoBFX1x8YeAVrGOopp4R95bLoPmwxgsosf+nT8pHJ/AXJMI5EC7KJUD+gQiLB0LESIECFChAgRIkS3QI9VTP0G+6SOkhpPoXHq3lSnCZUoGFAlfcJnRExFTZQnypZP7ja5+Kfwr1VSL9XWndyPOX+amptJvdhZuQkQLiSFUy0IUZd17ILo+AsMlbZjPizWkXvKF55j6VxaLHQS1WfswCOqzapiruZ2krIK/d0LVtGVCn8zViXVPNa4qA7ifSI8J9T1jvaX3maq6wNNUx0TsO0xhPJjYOmC6Q2+7ar3HPSdj8B4pTqQcOtirVMfOpWodda56RJqKtK2RCfaRS1cuNDmz59v1dXVtscee9itt95q48aNa3X+hx56yObMmWMrVqywkSNH2nXXXWdHHHFE9u8PP/ywLVq0yF577TVbu3atvfHGGzZ69Ojs39euXWtz5861p59+2lauXGn9+/e3qVOn2tVXX22VlZXZ+UgB/uUvf2nHH398QcfZFrrBVe8aNDdHLZLTwrMRxiUKx6cpagXLEtHtnVLD74URQvIxJQ6mdioiUAFHDMP7NFhTioJWNS/lj4qdVeQcM3qpbVoH84khPyJO1PmHLIn8lb9EVAKE2cmfMRDhxPnEHDMi/0qOZZAcUyQmIqkhBEmNUNIA1GIl3F/xo4kQZL5GSL/AVrC+46ewPd1zjWL6RAqIPwHPXeG2akTLIvSx7r/XiYSqBWxUrIfFpeQ2onlWqw4p+DKi3PZuhs7q/PTAAw/YrFmzbNGiRTZ+/HhbsGCBTZ482ZYtW2YDBgxw5n/ppZfshBNOsHnz5tmRRx5pixcvtqlTp9rrr79uu+66q5mZ1dbW2sSJE+3YY4+1U0891VnHqlWrbNWqVfbDH/7QRo0aZR988IGdccYZtmrVKvvVr36VN+/dd99tX/va17L/7tWrV7uPUUWPtYs69I4H8uyiKsEaqm9fd6CrIjEDzuCgYk2prIL5qBrenwNKeailuJw7jfJT41GtCInsl0htVduPEois+gfJDq/6JLKq9vuWFY0A2xDaNKKfoqjSYF5n3F1WVkxV8l9o1biaO6i2y+xoX1iCmheq3DuY10ztR7ViFUsAQaL11dO9Ds8iKquiDqLkmNI9R8eA3rEigVdzjFVVj1xD4BlztkHrJ1WdoOYYw/kkakDOIhThwhxLYhr0gZ1zr9fUbLLKgcd3qV3UJ1dOsYqidtpF1TfZgLlPtGu/x48fb3vvvbfddtttZra5E+bQoUPt3HPPtUsuucSZ/7jjjrPa2lp7/PHHs9P22WcfGz16tC1atChv3hUrVtiIESMcxZTw0EMP2be+9S2rra21+L+f2UgkYo888ohNnTpVOpag6LGKaUVloyVKthx+Sak7gBPhpPB+L6iiJwupoeC5TNug8D4RTHffqIBJ++5gWyWtVzJ9bXsQQqeCAJXUOtNUu6BAREJUs1TCKe6LB4UjfsWUSCjBS1O3IdWfkwgnHD+9/Ig4qQVBihpMhJPUlyDWSEQI6BjUbahG9AQ/OVNN/XFdtG+kttHgRPcOhWWEJhFmrZBa33bVlAL1I4+gklV1GvqTFljSEcRZQX3DQ9QLVVRaIekNqtuIajXXleiEqvzGxkZ77bXXbPbs2dlp0WjUJk2aZEuXLsVlli5darNmzcqbNnnyZHv00Ufbt68+tJDpuO8ZPvvss+2UU06x7bff3s444wybMWPGNivy6rHENJHM7/xEVflDgEiSDRSF7SnvlMb5XrBdqt73K6ZMXin/1b1xKNcTCSJ8CVMaAIXZVUVTJb/OQBzETF21n1HzTlVrICAhGEInNbR+vTufsP6CfTLN2FCXINoqyedTOQ715aXmZqr+rHQMVG2MeZyiZ6snHButi0LlKrksAuWOyCqGsim/SVRqCYVWYtOHVAYUArRfElV0IrBBnBqS1ADDd/xEctXcZDmf3J2kti7FtsdB7Lxyl1XHqm2IIKH8mpqavOmpVMpSKfdZW7NmjaXT6WzXyxYMHDjQ3nnnHdxGdXU1zl9dXd2uffXvx9VXX22nnXZa3vSrrrrKDjnkECspKbGnn37azjrrLNu4caOdd955BW9ra+ixxDREiBAhQoQIEWJbYejQoXn/njt3rl1xxRVdszNtoKamxqZMmWKjRo1y9nHOnDnZ/95zzz2ttrbW5s+fHxLTjoaXiVgGesHnoomKF8VUE2orqpjkt4a4L0wfgxA9VepTlyMM24seoEHab1J+qqyiKcupfpqq3yGF2ck/kxCkGwyFy/3Hq1b4q16MaphdVWUbKOQdYF/8y6rNBNSUCsxPFe9N2Yhe9Psk9dLZFzHHmlRPNcyuVtFTDiiBwvakrPrnU7xOzVrpNhUgrzlIyg+BIiE0nvjWh6OrHKXp2O5VUUxbUfPExfkKdaDYVggQyv/www/zckxJLTUz69evn8ViMVu9enXe9NWrV1tVVRUuU1VV1a75t4YNGzbY1772NSsvL7dHHnnEEpSvnYPx48fb1VdfbQ0NDa0eUxD0WGK6cUPCEuktJ59C+Z9A2KIR8qkaod895Y7Wp92BuarE3UYTDMTrGvOX7QUkt6YRLJpS7sAXi0A4Bisr3YGkMeIWa6BxPtxaai9nykWN+OfDXK8CW/6ZyWGwiGpYD/2zvcZadz41lK0cGi1HeZdqOFINUdL6aFo97Avloir2S6pFE11XuoYqgUWyStZFQMJUWyUlF1OtcKeKdiKSZC2ihtSJ6BZKQs1cIkrHoEINwaquDGpbXfXNih2yfPsit9+FaepzrY5DeDoDpEsRcvevO+SbRqLtzw/+935XVFRIxU/JZNLGjBljS5YsyRYYZTIZW7JkiZ1zzjm4zIQJE2zJkiU2c+bM7LRnnnnGJkyY0K5drampscmTJ1sqlbLHHnvMiora/ph58803rXfv3tuElJr1YGKaTkcsmqOYNjeDiggKZLLIfeDSUOhD5LICxtcmILolkMfq9zalMahM7AZFaIbe3qRwYg94sHeiynpSVuXqer+Khrl+6gAp5gmKiflIONWK3kItnlClBOIXZGBXi3/IBkvOARUJoX99inNBe6DmfxKRINVTJY4q/GSSyFo97IfaNQlJKDybaO9E2xWVWgLmtvrXJToBEJDoisQ0iP1cof7BHR0JontYJrABCsLUad2MmEZiEYu0UzFt7/xmZrNmzbLp06fb2LFjbdy4cbZgwQKrra21GTNmmJnZtGnTbMiQITZv3jwzMzv//PPtwAMPtBtuuMGmTJli999/v7366qt25513Zte5du1aW7lypa1atcrMzJYtW2Zmm9XWqqoqq6mpscMOO8w2bdpk9913n9XU1GTzYvv372+xWMx+85vf2OrVq22fffaxoqIie+aZZ+zaa6+1Cy+8sN3HqKLHElM/2grrbw1kuk8FUaSiqgb4/qr8FCxHJDcOK0uQNRRU16XBsJ16JaM5P3qWambPmEwf9y2r9nYn0Hygoslh+yBhQIIygAcZsInAYRU5LBvkuDryZd0IL1xUaTX3ArQaIquljKj6xUXiiGSVVDkhXI4hb5FcqL6jEEWSoYba/QQWlxPHa0xbgPNE1xCr4WvcaRjhEFNN6EPPl1biwT2H/qeUjqIqvLRvQQqP1PGpLTKtphhtS0Qj+v2Wu0w7cdxxx9mnn35ql19+uVVXV9vo0aPtqaeeyhY4rVy50qI5z8u+++5rixcvtssuu8wuvfRSGzlypD366KNZD1Mzs8ceeyxLbM0sa4jfkuv6+uuv28svv2xmZjvuuGPe/ixfvtyGDx9uiUTCFi5caBdccIF5nmc77rij3XjjjeiL2lHosT6mU+7+hSVKtoT5KsDHtLTEfTD7uxFaq0i4p/BLUNFP6F/sboPu6V6p/PlIHSXiW07jF6iZyZh7YGiSD8pCMuqGS7F6n76D1K/oOt8LIcigSdXxDRu0ZTt6sKaBV3EcUO1tsFc8TEuKuWiqKqkqS2p4178+fKGp7dZEFZ1yPVUlNEhup9qPvlCoBLGjVV8CHatf0VSVUNo3nEbd4MQ0CCX0bqanAQh+pJLXqZle9a6qo0EcCNQPzjYiJt3Bx3TNLUdbRXE7fUzrmqzfeb/ukv3+T0DX6+QhQoQIESJEiBAhQlgPDuVv2pSwuLflK6isQjPYJ39SCtt/DuJQX/jwJe9RCsn7c0w3QU4sdXQihbPZg3xSj4zu3f1IxcrdbVD1Ola+upNkBc6vVJDHplqVTVWfQXKiVEUnSBtRP1RTf7W4CL1IAyh3mC4gempSKod/faqap4ayad9IgcUuROL6sI2m6vIg3BOqmonKsthdSS6IIi9i2Ibin0rLlZCLghiiD5KfS9eaoDZdoDHB1yHKg3QnDBSrZvWYOyt2ElMjNWq6SFtjbJDUoQ5CZ7UkDbEFPZaYJhJpS+RUtseB1FGxEnVXooJWWpbsojY2uQ8rtST1gzpB0TQKqavGyRR65z727ny0Xb0zzdbb1G3eES1PEltodnSeZJoKgihcTv1sRYLpz3dMwgtdbQ0apN85VaCr4Xi5Vzrss59MqMdAhIOgkhX1hauGxtXipEIh20CJyxLwWOkjSbwnpPXTPSemStB1ldMsRON4Na2GxgT/Nqjgki4hEVhqSUz7odqlqYVYqgjxBSCmFotu/rV3mRAFo8cS01QqbYkciyiqwCfFdEOj+yVUBP3uSVmlQifyHlX8Tqnojz7S1K5MRC6pqIm6RiHRpaIm9YteUPSQcBLJ8xdNmQVrUxqk/Sh2EqICC1JqfBeXKsHVvMZ6saCgiK6Dal0jqihqXqh/u3Rc9ACo5GoTeaeKqodaqU7EjLahkGlVHVTVYTx3pCLD9SfVk9ZHZJJIuH8+Or90bxLoIwc9UMUPBHw2xWLCVBmsTyhEQisn9/ixIIqg5n/TONwAVmt0fUiBJbSlthbaBawjEbMCfEy3yZ70GPRYYtrQELNMbMvdk6F+z4C+4FlKZvr1MH59Wueebip+qm1y7+peyfz50GYKPlORIFIbUFFFJVeVDPiiopk+fZWTpyQOxL5jw6/0AJWlaqV6R9uXqP3D/S/EIAUs+MKF9W2Ea4OhV1KvYBuqYbtSXa4ev0rCCEjqRQVWVRFpXxTFlO4bIteq+oqES6iYbw1qyodCCNUPGgqzq+dXPf42jMezUFN0VD9eP+CceOATHUlCFS5GUcQQfartYq1WoX7UdjNEIgWE8rdRD/megh5LTMsrmixRsuWFSoqpCrpnKe+0T5H7YFKOKamofYvyB7pN8JCTd2gq5g4kZMRASiiprWSmjw8hhqPhxal2XPIrpEFyQlULEjWURbZCGLYUQ8NkSeSHmieoKg6qwkdklUhIEJKERMT3b1QaRXVMFWFqwJIMK/UD2BmJuahebb7tVUQlSHJerwuP7msAvrQD+Jh6dfn3TiQF5E1VeOkeVlJFWkMd2I+VAPlTbZoUY3vVyglILnksY5U/ge4Twd6q1fkI+KHfDVTSXATo/BSiMISJECFChAgRIkSIECG6BXqsYrru85TF67d8ORZBSH19ncvbG6GFaDl8HNWA2NArRV9RoF5CgdXGJv923XVR9yYK23MLUS0pBoukSLwTlVA5vK+si1RaudCHKvoDmDurFdik1CgheSU3z0wPvapV1ATFi7I1qFXp/nwZUimT4E9b6ypcqDbS8ZM6qprkYyqDO0kNyUdivv3DFBDKHSxcuYyo2yg0d9QMz3HEv8+qYwKBUrRUNRv3l3xMC3QWMeNcTMWzl9YFQHUUHVMCdKVTu/CprVC7GzrJYD/EFvRYYhqJennhe2pJ2r/SHZgot7MeqqQGAN+KIuF0t1ueaHvQpQr8KITeqQ0ohd6JcFLHkVTMDVulIfUghvl+7iR5YPIPiEoeqlkrBUc0uIpEmqBWpdO0TRAapBexQvToZa2kBbS2TSI1YNPjNbjnLpIJ0Bde+ZhA4u/ur0PoWgOdOyxWEi2U6J6gED2k1WBqjH+7ajie+qc0QC6ieJ4ovI/7q+aYyt2wBKg5xvgRRh86AT7+5FQLYfxTbfBgPioSRbKK95P4bKINlmg19QVAZ7UkDbEFX8w7pQPQ1BAzD1pztgWqrCdLTVJMi+PuQ923yH0h0hDhJ8TkMRqH5P8IfKWSOorFT/i+IbsoGoQDVG8rX9sqoVHzn7D4KUBFs6roqAVB/m1QricBc13FayPmUyIxQZIsXjPFVkgsGvPq4MWcguMCEuLVuudYz+0MkCenkF+RvNG18WBZmSATaH1EfuncKfdEEPUpyH2oklqCao2nqJL0gQzCBM0XSbgd/bwmiiJAS0NCDMZTio6RDRZFwtqyn+oOdlHRaPs/ljq6Q1oPQ48lpiWl+cVPjQ1koeSyy9pmd5CsALuo/lC97zfJN2NLKoKfEBfBFxmF7Ukx9SB9AE33wS4qTrcM9ZQP0LrOS5MVko9M0oDVQJY/FBaH+bDVIFwcUQkLNDDRdv0FG0GqnpHAQfgYVCQsiEHjbCA6QEyIwFga/Bh9BTBeLfnTEpGg9rNAmtwlA/m9enQMpErS+VQIXIAQPQEJrHoMhCAeqP5jUz9egkC2GhMtuUj0QIJZYNtPsYreo7FZVS7Rs1l0KkHrPrGxR+7+dQeVNQzldzq6wVXvGjQ1R81ywuh9+rkvZjLO7xWAb1BVPimwRGD9oBdJMup+9RK5JNWTliW1lQchUfUU/T4jQLA9/0Cs+umhWT+pmQH6uNONolqjqMqqkIuJRAJJDoRjiTRSBTIQ4kgCrhcQLk8lK3Ss/vtd/WggqCQE1ucJzS/MDK+rR36XotooKaZw/dV9s2J3nMCMePHDhFRpucpfUaXxg0tafTCoebd0f6qm6/5xUo36EIlTU0/U95pqsK8SWCKrucdG43dnI1RMOx09lpgqIC9SeqeRmT7lkxLh9PuTmrXW1cnz/bvCmYeUUFVFRRJKoIFCDI2TEooktGGDuz7/YIq5WVT8IbaVVLvyqK0LxXxCJLBKCFH0LPVU4gt5ophjR8dFy9KDQhZSWEwC2/CTGtX/VSycofA2QiXXqoqmfvz4j0MtpBKN4+mjRr43oRsY6vkyqcufD9MMgii3dAnVlBrVOV3+CCNbNR8RVS2aaAxHAgu2UmIuOnaSUiyvzMxrhhQCgxSC3OPoaN/oQhAS005HePZChAgRIkSIECFCdAuEium/sanWVRFKi6EwCaSAdSAYVSYpB9RduBbcAIohXNiQzl9fIupulNqKRuDbwwMlwIMQDdlKcXcVrWpUVkfJQiXtC91TCGiTayZtVOhCoUIodOGWXmIVMSgmpPJ42D++7apstahFzsUjZY0cA1QloFm0kFLD7xmh+I1E9CDKhZrHqc5X6LGaucdL6SNoPSYa7NN5wmnuJDlcTGkgSm4rFkhSwZ24b/h8dXQBI4BSGZJCL3tSRzGvWxybxWgTV+8X3g0Pu1ARcp+n7lD8FCkgxzTs/BQIITFtJ0rj7uCaoNRGKGmnUL56v/tzUbnfvftiasq4oRwipqmIO2hQagDuLnrbaeEdJKFqhyg/iIRRrpNKEIiEinYxGH7Utiq96DAtgKA6C6gtPtW+8HKXpwJ7vqtkQCX+MmkUSZ26PkxlEK6t6rGqWl6pRX10XcU2rRHIY5XuY7XQD5cNEMpXK/XVFAW0qaI0GP9HmNguGUmo6B0N6/NUYwGVwNJ5b6uOIQzl90j0WGLaUBe3dM7hp1Lu13EDVOB/AiJSX6jAJ0GjptG9WSuS7nbXNboDWC/ffI1pd0cyUXddRbEyZxoRWCKhNJKQ6innO4FdCNqZoC+e30JG7O1MhSRYJCWahBPgpY5KkNq6VBnUiEjSNCIwQfITqe2p/FIXe5krhu2y6hdAuUBrMHFZ8HuV85jpmvmh5tgSsC88qYhi0wVxu7Kjg38blBNLhJY8VtEaTCxWonNM13VbQyVnoum+umwE2lQHAeWxtrmN7qCYhsS009FjiWlZeaMlSrYcflmF+9D0SrmDVRGcMSp+KgFltSJJpvjufH4SamZW7Osko/axpwr8eNSdLwa3ApLVBugfDgoneeXhIEPedsoAWw/7ofbAph7oRBrKwIsPXmBktYRQiRPti3+aQl7MuOBIJaFqeDNIQRAB+5trqpwDubMU3CcBuhfJ553uCUqhUBDA3kpWJdUe9QS8/kJxVhA1X3XHUNVhfE7E7arPkz+FSDWrV0kcWTmR1VQE0rEoHC+G96lwCq0B83arG3SGCu2iOh09lpg2NsYs00ZlJ6meFQl3EC4VzyKRUIIyziPhpLC4COr8ROkCcqemIC8JIqv1vpe1OvDLIVUYNNdD/qsKtcVnvbh//vWRwT4dP6q+RGCIcJJiGEAdVQmxkn5A86hkXVHpWptGICKJ+Z4BFE3/8RbaytaM88SDoKPb4/qXVSvm6byVwsel2uVJzjEN4GNM96yjGIudnwiq5V9E7CRWv96dSO+dAIphrooaiYrj47ZENFKAYhoS0yAI9eYQIUKECBEiRIgQ3QI9VjHdWJOweNOWL73yCje80VTmfq3VNJEXKXRSguInf2V9e+BXSGPw1duYcZUbMs6nkH8aMt2pTSmC8pNg/zwqalJRWpn/77oadx41lE9hYbGKnuBBbq/c714tTvJPo6IZVNrEMKtScGSmq41BWnIq+xykuAp8NwM5BqjqiJrHqLgrqAVshCD5b2okQFXRCcr+Cf6nZqaro0E6qZGi2ZG+uEE6RtF86nVQDfFRvYVQPuSTUt6p19yQ89+hwX5PRI8lpvFExuKJLQ9oHHJCy2H8olA+GfH3K3IffqreR/cVeM/5k/0TkK9Deadkpk85plj7RO9bGtSA1KINlBp+om34Q0hq+JTySVVzejIdV/NEKdSukh/JFF0M29K6qICDciKxy5XY9lXOp1PzOH33rGrbhcVFcP+LHyuyDZRq3dWRtlpqqoCaJ0ugjz86LpXAEvxkUv24pG2SRRs0hMBuUx1OwsXcXv+zo3ZRonC8SkKJ/NI0dV+oSYbBfUe2gjkFsZG4eM9sS4Q5pp2OHktMi4rTFi/e8lA0NrgPFxFOIpdEYKnVKPmTxiDvNAk3tT/fU+3yFAVv06aMO6iTihqhSnVqodhIfpeUnA+kRq04VfLOasDHFHPxxIp5NU8S8z3Fl5D8svbN1ywWA6l5t9iSU8w7a6JWqAFsipT5VD9Rgpr/SPd6rXsPR1IiWVXVUSTJvntHzTHFXEz68BPvJ9VZgQZP8Vl0llWJNF0vtU0v3Ds0JpDlldy9q1DXCLXQSSWNqtqq+qJSW2mwkPKgLoL2JVRMQ/RYYhqLZSyeQxQrerkPAPmrUyh/cAlV27vLUqvRJvA2rYfe8+WJ/EtFSmhzBkz3Y1r1fiRNBAEGjXpQQrFvt1b5KXv0+V9qqmJGSqhKQlV1DEPZIvlRq+EdYqa2RiQyTOqwkD5gZhH6MguAzCZSTCAMWOp70eF1BRJCpFEN25INWEysSqfThGQVliVF11l/AJVehVroRgiSyqG031UL6VSCSKk8ZJJO11C1kFKjCP59JsKJirxY/Cq3hhXHZkrbIhIa5J7oSoTEtNPRY8/ehpqk1axPZX+NDTHnl/bM+VUkPOe3tiHi/DKeOb91DXHnl4p6zq84HnV+fmS8tPOLReLOLwL/S3tNzs/iSfeXbnR+keJK52fpZvfnZdxfNO7+Mhnt50d9o/uLx9wfgdbfMvjk/pqb3V99g/uj7SYT7o+2S8vG4+7P2be09lPRlHF+kUTU+anLErxNTc4vkoq5P9puY1P+D85lJBZxfl5dk/trSru/2nrnh6Dr1RLqy/0p97BZK8vC4OEHXWu653CbcK8T6P6n46J9oW3Qs7Op3v0p+0brp3MJ8JqanB8+c8pz2NqzSKDrqqwPjx/GUhpz8QTAfOp4rS6L43iz+8PtCte/ExGJRAr6FYKFCxfa8OHDraioyMaPH2+vvPLKVud/6KGHbOedd7aioiLbbbfd7Mknn8z7+8MPP2yHHXaY9e3b1yKRiL355pvOOurr6+3ss8+2vn37WllZmR199NG2evXqvHlWrlxpU6ZMsZKSEhswYIBddNFF1qxGqApA11/1ECFChAgRIkSIHowHHnjAZs2aZXPnzrXXX3/d9thjD5s8ebJ98sknOP9LL71kJ5xwgp188sn2xhtv2NSpU23q1Kn21ltvZeepra21iRMn2nXXXdfqdi+44AL7zW9+Yw899JD94Q9/sFWrVtl///d/Z/+eTqdtypQp1tjYaC+99JL97Gc/s3vuuccuv/zyjjt4HyKe3NvwPwM1NTVWWVlph97xgMWLt3jcDRzkhsGKit0vgi9XkiG+u51BEN6vKnHDQOUJ94uYooVlvpy9VMz156MQfQwqIWk+mmbNoBpRNTwAi5+oKp+mKebkNI9YJIG3vNoXnoqpglSIi+bhXq2QAwgKEYWevVYUTWV9XgPl7BWYO9caaP/82wiQZoDHL547BN0nsgODmHeqpG6oaQGy20AA3SJIyN+/bKHd0VpbFoAOHGpbUaVTWTv2xZlPHV8od1TNJ1VzUcmzNEAualu5rTUb6qzXiLNs/fr1VlFR4c67DdHCFdY9O8sqysTiwJZlNzZYr0NubNd+jx8/3vbee2+77bbbzMwsk8nY0KFD7dxzz7VLLrnEmf+4446z2tpae/zxx7PT9tlnHxs9erQtWrQob94VK1bYiBEj7I033rDRo0dnp69fv9769+9vixcvtmOOOcbMzN555x3bZZddbOnSpbbPPvvYb3/7WzvyyCNt1apVNnDgQDMzW7RokX33u9+1Tz/91JLJAB3HWkGPzTFNJtOWyGlDmgF7JyKcBDLYp6KmNOSTEqhIKuIrYqJCp3hUe3jiHg1MMKOYeI5dnlQSqhaJ+ENa4gsCK3BT7oX11Hw6uc944dZAtM9+MkUkjEgjVevhslCsktng7ke0nOxioBsWkb9GKn6gFyKQRP/5pJzYFBzXJrgniNSmRNIAwNzWQNZIRGB982EHJlpOyx1G4K6JRFqdTyHwSnckM/7IU23QiJjS+tSPC8qBp1xUIr9O5yuxkAxz+OlYRWJKy0I3KF62g+frSgTIMa2pyRdyUqmUpaBNbmNjo7322ms2e/bsnFVEbdKkSbZ06VLcxNKlS23WrFl50yZPnmyPPvqovJuvvfaaNTU12aRJk7LTdt55Z/vSl76UJaZLly613XbbLUtKW7Zz5pln2t/+9jfbc8895e2p6LHEdFNtwuKZLQ93HMhgPbQpXdfgvhCGlGgDfV2ze3MnwOKlBPbFr2hSZT3ZRZGPaTO8+OLUbSnhDqSohNLgQon4NOBERfsZP/mTWz6C6kc2UGoBk6qOkFcmHJfXQFZbQHR8x4HEj8iaKLbQ+qIlsDBsIwPkjwgsOpLR9amHc+JfDogkEUQkoUTWRXWUCHwEehLT+YwUq5X0Asi2Cb1txTww1RopiN8tkjrhvKskXC0uhOcVK/Cpep+ADgGq2i4URKlkmMZc1QaqM3rS03tCVWC7EgHsooYOHZo3ee7cuXbFFVc4s69Zs8bS6XQe+TMzGzhwoL3zzju4ierqapy/urpa3s3q6mpLJpPWq1evVtfT2nZa/rYt0GOJaXNzxAyIYi7oXVUFJLQWnrfeMFZRVX4p+LQ1pN39KvFdqVTUDeVTpT4hTped2uqBwokmyRF4SVIlKSmwsmG71+Y8sjoK88mWL/QSUivEKawOJNSrhZdkr/ztosUsEDoDhwf8jEq6x0qEk4gkgY6BtiGDSLcfREJpf0nhJZWSwvtBSCjds+TjWCwQTLo31QYTanW4SgiDmOmTrZSfYKuuB2q/e0KQ1APVsxTPp9AAQiVFNL4mQaVVraGCzEeg+ci9hdIAuhIBFNMPP/wwL5RPamkIF93sDug8+H1M+/R3SZjyLjTjkD+NJeRtyn6nXIWfC+qsRIopmunTABakKxN9CdP6aLuqt6fPCsdr0HwXvTraD1h/oSkFxiSU8iQzm4DAUFgdCCyF1R3QDUuKJBE4WhaOwaM8TiR6LpBMA/nFHFP/hxMQbiS+lD4A8+GrH1MUaEYXXl2ArlGKm4KoyHM4WsxFVXOiVU9VVZWVjl8ka2o+reiLyn6/os9qI0WWhFewmnpBUJuaqLmo6vqCkNpcskrEtbMRQDGtqKiQckz79etnsVjMqYZfvXq1VVVV4TJVVVXtmr+1dTQ2Ntq6devyVNPc9VRVVTnuAC3bbc+22oMeS0xLSposkVOMVF/nnopeKXfAUeshmsSBk9RRyjEr83WcInWUTPcp5J8A1RMHCCCSmE9KUE2hyShe6MITKXZTFLxaSEcgKJ2FzHQSCqBwdLQXmE6TYkTwkaRIqRZmJwKnhs8DtdoE0DbIiB/TCnznDlVlIM0ZUm5pf3E/tMYBEX84wwzJtaq2Inz3P6q0GN4FMqi25AyiQAZpMOCfFqi9LSxb4o4d8rJBCsKodamyXbXgSs6TF/dDJZdqHqtaEJU7X3dQT6ORAhTT9hHZZDJpY8aMsSVLltjUqVPNbHPx05IlS+ycc87BZSZMmGBLliyxmTNnZqc988wzNmHCBHm7Y8aMsUQiYUuWLLGjjz7azMyWLVtmK1euzK5nwoQJds0119gnn3xiAwYMyG6noqLCRo0a1a7jVNENrnqIECFChAgRIkTPxaxZs2z69Ok2duxYGzdunC1YsMBqa2ttxowZZmY2bdo0GzJkiM2bN8/MzM4//3w78MAD7YYbbrApU6bY/fffb6+++qrdeeed2XWuXbvWVq5caatWrTKzzaTTbLPSWVVVZZWVlXbyySfbrFmzrE+fPlZRUWHnnnuuTZgwwfbZZx8zMzvssMNs1KhRdtJJJ9n1119v1dXVdtlll9nZZ5+9zVITeiwxbWiIWYYqMXNQ0l8LA5HoVQxnthhyTCmUX5ZwL3ZRrCzv36yEutuk4iej7k2yXYh4y5CtlNr2ULBVkfNEVWAem1b5jjZFoBhg5buYx+nvzORBaB8VSSpCogIeNRQAQLWVlEXIu/OMujW1rSzidSgK0OWpCNoAQ/ELKZV0LTCtQLSpUpTaCDgQyKHyIC00Mf8bjotC2XJ+pjIPFWGJKQriOOTBOcGCKFKR1cIx0S5Ogrocqp4wXwbSoILYTxWSx9odCqE6qfPTcccdZ59++qldfvnlVl1dbaNHj7annnoqW2i0cuVKi+asd99997XFixfbZZddZpdeeqmNHDnSHn30Udt1112z8zz22GNZYmtmdvzxx5tZfhHWTTfdZNFo1I4++mhraGiwyZMn2+23355dJhaL2eOPP25nnnmmTZgwwUpLS2369Ol21VVXtfsYVfRYH9ODFz6Y52Paf4Aboh7W333hDCl1TxfZRe1Y6Q7+REL7pNyBqRzGuYTPCqokXunMkwRvU0KM8vOoKh9eCN7GT9351AGnATxA6SVBPe/9m6TK+nVAhtWe6lA4QhXzGC6GtppqbiOSOgjJ+7eLBInCu51BONWKdrVlKMA5fiKm4CKQWQ8FfDAfhegxb4fSJQKc4yikAdD9FC0rsE2pStaw+Ekkv6pThUrW/FCJL40lZTAmqh3RVA9USCuS0yDomimkRs3rTdIHAuUJw3lSx3V0WxHna6NwqmZDnfXa/pyu9TF99TKrKBPbzrYsu7Heeo39fpfs938CulwxXbhwoc2fP9+qq6ttjz32sFtvvdXGjRvX6vzr1q2z733ve/bwww/b2rVrbdiwYbZgwQI74ogjAu1HMuUOVqSEUgV+BYwRDWl3wCFimgFvU6Wbmb8YqrVpfkJrZnzVRbsoHMDIiJ8KnfDFBDsDfn/eBh9ZVXOsZHUIrgOQJvLFxCIZKjACskrrIxLirI/WT1X/agUfgAgc7W+mgXxBtaEFz6dCEqlojNZF9wSReto5EuCFjwYzVqrpmmU2gGIOZNX/kYRV/6o6TFZrqtqq5nuq5K/Qin7VLgqff9E4H/YNW02quaiq84H/PYGFaQHyX8lWSnVMIXS0YppLnIlEdzY6STENsQVdSkxbWnAtWrTIxo8fbwsWLLDJkyfbsmXLskm2uWhsbLSvfvWrNmDAAPvVr35lQ4YMsQ8++MDx4FKQSKQtkdwyUESBNBKIhFYm3WVJREnFtKr8CIWpfKBCp1iEksthYVIuYYDwaj+D9ZF/Jql+otn1J2thBwH+Fwy9cEgxUbtBgTpKQBWRIJrOI/klCyl/KJ9SAKC4hoD7IYaU8camLzj4HsLtYqOAtpVqeV10nsifVQRuF1IIKLyPqQYAqVsXkFBZkVajo6pjgNpdSiW6/nFC9SdFhVeMmIhhdnSbUP1OaRsKIVYN9vF6AeFUw/F0bVIgVqjj/xeVrEUKIKbdrUnAFwxdSkxvvPFGO/XUU7M5EIsWLbInnnjC7rrrLmzBddddd9natWvtpZdessS/B4Phw4cXtO2G+rilc4gcdX4ikAC1qdldNkWWP7BsXCTEftsnqspvzriDEOaY0kNDnqUJqHzfsNqZhpWT9CBvAuJYUepOg5eav+IYz5qaw6YqSzQfheOp8ptAxIlyRWG70Uof0wMSQuqbs5yZRcR7Tg1lozpM553UxhLK42s7f5A/JNz7EJsEEMTqeMwnFq2r6DzRslJqCHXHIhKqKpdq6L1ZVKUp1K4qhso+KzZLZq2QUFFZFT2LEdiIQLTG8++erCqLPsEoGsDHBRFYsgFUx/9CrKa6A8ELYBcVojB02VVvacGV2wqrrRZcjz32mE2YMMHOPvtsGzhwoO2666527bXXWpryDf+NhoYGq6mpyfuFCBEiRIgQIUKE6H7oMsW0kBZc//znP+3ZZ5+1b37zm/bkk0/ae++9Z2eddZY1NTXZ3LlzcZl58+bZlVde6UxPpNKWgLzSXJBgRDmmfcWOdOUJd3uxCKhIkMcUjcS2+m8zsxj6k7qTsNo87aqZEdgG5phSasCmtguYzIy71WAnJSGUr+adUlGXWlmN/d5BvSJfTOrbThXyQiU9LUeFNKgsot+pqKJCj3JMA6AHBZbNrKP0i7a3QeoghsAp9UJVGzFcLiqrMA2VZVFZ8acBcCoD3Nd0f5GKrCqratERKqaieql4papKazxA5yexxTG7d0AqRzGEwWl9ihoc5HrRcakpWqo/NXqbwrJtbaMHVeWH2IIuL35qDzKZjA0YMMDuvPNOi8ViNmbMGPvoo49s/vz5rRLT2bNn26xZs7L/rqmpsaFDh1o04uXllW7c4A5gvUvdh59yTNV3ejOkC5DBfoqshnzmCRmw2fFwgND6E0diYP5OZvoUtqHBpQhSCBrJON8NDWHFvVP8Iyb/q8VPZAhNgPkikLSHnZpgn9F0nchko29ZInkQAmYrKyGHsbVl/fthrRAusUMUArZhfrIOqQwepeOolfUAsq0yyP+VCaxYva+cOyyQoxxTNZVBNmwP8NwVGpJWSRhBJpzi8y+uj1oh6+4FvudYdUdQgdZ4IgFUQ+tqGgClEOS+T9Tw/7ZEJNr+lILukILwBUaXEdNCWnANGjTIEomExXL8R3fZZRerrq62xsZGSybdmzyVSqEJ7IaapMWbtkzvCy1JqaaD3hn0nNc1uzOmoQI/DgMCVdc3+QhcUaKfMw8VROEDUi+mM2BlqdgijuarAWUVQMUEnl+VwZchDHww8HsbXMKNhTOiOoaEEMmfSIhIRfERWNwmkVzIf41SxbjYUx7VRncuucAKgQp0/vVB4k9KKxWX1YKaRQ4Eou+omrOMubOQY4rb9V8LsagLP3LIgYJIMxI41e+0wPabtF2VhKkktFA/1Vb2xYnmmFmEjrVQ8qtW+IuFnjif3OUJristq7q8tJXvGg/wUdJRCIlpp6PLiGkhLbj2228/W7x4sWUymazR7D/+8Q8bNGgQktKtbz9j8Zyq/MYGd8AZUOE+NERWyceUippo6CMVNROlkH/+RvxE1cwsEYUH3xMHEvhyjcAgJJsPpSDkXwRkolDTfWw1CMcPAziR0PQGKv7SwvGk3hmRayx+0aZFfDeZqkiqLT9l1VNEFMx40RpJ3EZmY/694z8fm9flHisRWFpW9YANokpiAwSo1Mdz4htPkLyi9RaQdSKhQUKPQUziJaIvFjDCh4nsIoCVqaKPKXW/UckvpiT451E9UQM0XQjSupSgErMvAoELQ/mdji4N5be3BdeZZ55pt912m51//vl27rnn2rvvvmvXXnutnXfeee3etj/HlHxMiTMQCSWohd8p6AYVB4LpVOXDA015p/jSEPOJPOpjT+EYCtt8DjZQYoW81wDb9YfBNsIXOcDbSLY9qq2S9rVOpI62GwT+nE113zD1AMPbQFap2pzSTIisqXZGBYb8PSIrWAms2nbBcYkNBjA/Gc5xtBL8eWEbyrUl1RtVf/X4i7WUEszFLdSL1MysWfQj9SOIWb3qbaq6CFDOOqQjYY4pvij81mAwi5rrqxJYJM20a+J8RGrRK7WNnNXukGMaiRSgmIZV+UHQpcS0vS24hg4dar/73e/sggsusN13392GDBli559/vn33u9/tqkMIESJEiBAhQoQI0UHo8uKnc845p9XQ/fPPP+9MmzBhgv1//9//F3i7dbVxa8ps+ZqMRl0FjoSbT0AwHAJR6+K4+6W3tt493X2hJSnlmPrVUGUeMzOLt9FZIzsfKDDwtepR7hDlCVFYnVROLByAQizFP5DC9pR3iYU+WhiYKt+NwsWkXomhfMXvEvMVIZSJHqtURS97u4KKRA+KWHOToVxJOLaoL1SBoWyxIUChpv6tzkcOAbSs6BBAaqiU74xdrjRXBjyuAK1W9S5EpMD6tqt6jGKRVJAOSYUXGKHpvlxg5m8wAPtB6qiaw6s6GkA0D0GzRcRlvwgh7zDHtNPR5cS0qxCPexYH8piLvsCtaKym1CZCRZLSBdwVJiEM4CeiCbCGwuInCp+IBvsYyidQuIVIqJgD5X2+3p3PP3DigKaFt8miKFLqnk8iCOnVrg0WpgbQy1804pcquqkqnXIMKU+S5sNKXa3Qh8LFZF2ktvMkoP2Ufx6xAh8bEdAHB4AKuORcXDrH1KZUuD/xXqLzS9X7YpczGYGq3AXyqxriq21KiUmpXamomBKKn2i7coco/7nDfFpxXWo+qWohRSjEBmpryN0XNSVkWyIkpp2OHktMU0XNlije+gBNY38VuCCpLjhEQtHHFJJ2YpHEVv+9eRrl64DESwOOOo1yTAn0QqBWoHVkISJ4SsKAS56NBCKh9KKXLZQAbN0EqiyRBCJYvn3J0L4RuRJzDNkDte3WqK0ByTXmE1MbTVif75mQK/ypUl90GyCgsoztYUWiS5X0pKw6nZ9EF4k6sVMRASv11fJHkawq6wvSRQeJDamUYn6qSsLVdqZkK6W4EqD6Sm4eop+sWqlPCEIe23J5oeheZyMsfup09FhiWrsxYfH0lkFh0HauEgbCilG3QCqIojE9AZX6G5vcQSftuQTO31qUij/SUIEfI0N8ethpgCBiSl+9Sr9rM3xYqSDAqxVC/rR+aGBAiFBVOtogUTheUwJVoDm98LKmVqOq9ZCa3oDLiuRXtt8iRRfIarSPTzGkBgNwXOn1oD7CB4KqeiMorYJIglh0ppBaLCSj5VRrKDKJp20Eed+q44T//perzQvbrVa3oZrfi9ZY1DiF1VDfskhyRHVYPU9qC1GaT30nqOhuLUlDxbTT0WOJaWlZkyVKtpCxjTXul+ugfu5LDVxw8H1DY1ox5OxQVX4xdCvxq6HxiGiPRTmhMJB4DRvc+Yis4jQghJQDpXR0am19flBuqmjvRHmN2CFHJCsq+eGwujaAO6SOQvlYHQ4fA6oXJ5FmUqXFELUMoVKfrmsaiZSmtqGVlUouYbukhKOnKuwLEsdEYYoxWl6hRVkAv0hVlSw0Z5PGAzXvNJDaCudJyXW3VkgoQcn3BEFDMuY3M6MAl5pjSqkHRLiChO3b8k/tFlX5ITHtbIRnL0SIECFChAgRIkS3QI9VTP2dnyp7uWoTNcj5aJP7tblbb+re5C77KVTl71ihfYE3e/n7F81AOBK+0mKpMndl0PkpknCTZ7HFKXXvyIiFTvCVH6EQomK6L3Z0wqpsCHmiEojV5u55j/Vxzwn2gAelIoodh9quLsf9pbaqYrGO3L2KzomqSgXYF/OdJ2rJSQp3eq0rGUXFUH6g3vbk90nzkQG+kp8qFnqp3csQomenXIijdjBSlqNtqp2qFO/Q1rYhdlxCH1NSIKVzonal0lI55EJHUv0oDSyIET8hd30dve5CECqmnY5ucNW7BtGoZ9GcEMnna90w0KoylzQOLnMf6k3N7kOdgAFncKm7vs8b3GX7F7d9U1M+aSpa6s5I71F62GFaJOmuz6PiJxroKKwGlfrYzq/UzYt1KvXryaJJ7TsfIGxJIXTMnQTCJVZDY/6kP7eTSKPYvUkloQQkq2iDJBSwWTsKzPykSzx+1XopkB222s6TAJ3fFIP9iL+fvOmFeZhSgHZZZD8ndhJSiaPYrU1al0p81YIgZT9aQ5D2qH6SqOa/qik1ciMCsqQS0zHouBJi+lk6Z2xPByje6zAUQEzDYHQg9FhiWlSctnhOVX7f/i7hKk+6D3Vp3J1G76VUzJ2P2pSS3yl5lPoLcaj9KHd+grxDajVaDxZN6sOoVnmKFi+emlPmXxV1WxJJKBLYDKm5YPlFOaagwEWBEOA+i9Xr7r6J1R/Y0Uls00nkhz4IxMKhaAqcCkgx9a+f2nuS6wF1/kKVWitqU4vkEGoyOsG/f6Sqyiqq+ExQzm4Ab0+5utwPJGYBKuaDFOao14vGNSr+VImjH/BhjudXvV50HbBrFH1wiipqWuz8lKeYBrjfOgphVX6no8cS04hPMY1SgjmAPEvpXU3jTQKsoahSH22fCoVabU8hetUaih5CGjgJcv/o/JcktloUe5FjkVCjuB9wA6DVEvlTAklCQkRFR/7jJfJKjl9A8qJq+00APSXcalR7oSAJpWIi8l717wc+dKJPKhJTIOYUthWVahmYQuJbH3xwefQRlgRllY6V9pecGkyLVLABvObU4YwJNA8prfAh2eE+mAFU1EiM0gqout53j6EiLVbC0zkhUCMWdbtBQINWLjHtBiHxSCRqERJ92lgmROHoscR0w7qkxRu2nmMaq3BfmuSWQ+/W/sXuy+WzBndhUkxJbfWT1QgEH2kaPthq3k5bFZMtkPO9CvQxNHPCbxGxOhZf4GRvRAnFpHoQqSUo5KK1+UjR8r38VSufKOW/gsJLQMJBZB3zTkVCAIeKOZa++4SuYazEDdGiIk25rqLVEvndqucJP5KIcFMOrE+VxXsY1i+lRVgrKRqgBKsfHPKYQGF1/7OtKq2qEb2qwBJofwMpsBRC9x1bkH73uE0x9QS3IUZl6D0hmjfkbbc7KI9hjmmno8cS0z796i1RsuXmiUOnmjp4aX5a7z7UO1e6AyKJEoNLtIc67bmDbmMmv7AnHoUBUoVqnI9hG1h2k6isqoUTSutSWBfmEyr5mtYKWaPQKBqbaxZKHAYX1ycAnVXE9quomBK5pF0Tz5Pq2cmkK3+fIxBpwBxe1S5KbI2qkj8usIP9E4meX9Glex3zblUbNAIRPfQdFQmRSlb92yDlDkPUYtSDSC2tT7VkUj+u1c5PyvHLBWeFpx7I14vyyVVRg5hprv0UWVF1NkJi2ukIz16IECFChAgRIkSIboEeq5jW18etOSc83m+AazVEH8JU/ITOPfChWpd2vwMolK8UNmWgKt8MVFTq/FS3zp2GNlCiEkL5XjUbtWXV1no+YI5pgF703EKzwMKUVqDmU6LaWCAykHog96cPYpIvQs3tNFXl80M8BrbtEu8ThJjvLOZF+5V1dTm5A5cKfF4pNA7LYqEPzefPMRVDz7R+VW1Uj4sU0xLoU61CyTFV7aKCpChgnQDlCVPKC+wfKYZqh6jc8a87KI+hYtrp6LHEtKio2RI5VfkbN7ihscGDwQMRxsjyBHQDonQnCD9SPilV5RfHyvP+HYu4gytW5asFTDQfdY2iHCMKvUN4n7z9cPATCLFSpW7WCrkIYNukbgPbg4okCa17fONcBizK1NRh2l9aH7UujMKHGTdnEdvDqnxTIVPq+aX0hlLICU2LVk6Ui0pkIgb5zrTPlD/snyCmgFDYXrUGk4rwzLBrFBWJMZkUbI9Uiyq1Ap2WVQkcETPx45q7fBX4wUXbDHJOiiA1RD0nqt8p0Q0iq7nvoiYtH36bohOr8hcuXGjz58+36upq22OPPezWW2+1cePGtTr/Qw89ZHPmzLEVK1bYyJEj7brrrrMjjjgi+3fP82zu3Ln24x//2NatW2f77bef3XHHHTZy5EgzM3v++eft4IMPxnW/8sortvfee9uKFStsxIgRzt+XLl1q++yzT0HH2RZ6LDH1o4Q8RqFuoi+Ig+RjGiWze6jKV0Upv8E+Vf15HuTm4ZcrmJOrnqVYYSMaYFPhEKmtlLOayb8+ZLCe/thVaVlZJd9NrY85EWIkBGANFRU9QJmX5x8H5VgSQYwktGKlNJAwyrtGtZWILvUXAFKbaQLXAKow9G9XdDNA0HVQi5oCQL7vwDXBgVj8pKqj2DiAtgHKsuzPSVAGQFQCO6FVZZB8WrKGUtuUKsVfao4pEU6COoarLUnjsF1yiKHt5i4bD+Al21HoJMX0gQcesFmzZtmiRYts/PjxtmDBAps8ebItW7bMBgwY4Mz/0ksv2QknnGDz5s2zI4880hYvXmxTp061119/3XbddVczM7v++uvtlltusZ/97Gc2YsQImzNnjk2ePNnefvttKyoqsn333dc+/vjjvPXOmTPHlixZYmPHjs2b/vvf/96+8pWvZP/dt2/fdh+jiohHbOY/GDU1NVZZWWnjr3vY4sVbyNiQL7mk5ksDXYI0pNQ9XfQeHVziDmB9i9yHbFCJ+7BWAkn0gxTT8kQ/Z1qkodZdGAin1wRdk2jQ2LTOnbYephG5JBVhA+yfUGBFXZ6IXOjKKlgUUf90IGbYXYgKrDaKTgJ0nnwqKnp2kjUUWVmptlowH7q7lMELjBoRYEGYWNHuJ3BiFyl1m0jMwBpMLaZT1XH6gFGKk9QOVGgXRctSAZesrIoG8CqZ8p87IldoJh/Ax5T2TTXiJxUVgMS00Cr/IE4otE2CqlSrRV1qKD+HmNZsqLNeO11o69evt4qKCm07HYQWrrB+9f1WUQEpcVtddpNVDjy+Xfs9fvx423vvve22224zM7NMJmNDhw61c8891y655BJn/uOOO85qa2vt8ccfz07bZ599bPTo0bZo0SLzPM8GDx5s//M//2MXXnihmZmtX7/eBg4caPfcc48df/zxzjqbmppsyJAhdu6559qcOXPMzLKK6RtvvGGjR49u13koFD1WMS2raLJ48RZSGIdcT9XxpwKeSwrR9yvSiIm/At/MrCRemfdvIqZ+VdXMLEG5o8QuGokgws7RVy9BzTulgYnC+/4XQge3X5TD9kTq4AVOJFElegT/B7gHKoUHYVHs8kQf85h6ALNRdKBAFwGzVs6nku+pdr6hbdK9A/NFMkAa6aOB7h0gtag2kyXZprYJRkQkw3JHK7UrF5FmtZOQSib9pEv1DlUJslrzq+axwnwUosc2pZhW4NtnNaymknBKvcIOXLQN8Y6iwQMV0zYM+zNfbMW0pqYmb3IqlbJUyv0waGxstNdee81mz56dnRaNRm3SpEm2dOlS3MTSpUtt1qxZedMmT55sjz76qJmZLV++3Kqrq23SpEnZv1dWVtr48eNt6dKlSEwfe+wx++yzz2zGjBnO377+9a9bfX29ffnLX7aLL77Yvv71r7dy8MERZuiGCBEiRIgQIUJ0MIYOHWqVlZXZ37x583C+NWvWWDqdtoEDB+ZNHzhwoFVXV+My1dXVW52/5f/bs86f/vSnNnnyZNtuu+2y08rKyuyGG26whx56yJ544gmbOHGiTZ061R577LGtHHkw9FjFtLEhZpk22p1RShhNIyEkBZXVG5rc7fVJuSskNdSfcRGLisVPaqKGKt0RKNRW4yqwXpNY+UrwG6yTOoQ5dvDFHSDvLr1BU0zJOB2rrVUV1beNZsi8iFNxMBx+cyPkk4LCTyJBAoqE0nXQo14skqIuVOhU4F+uzFUd2FkBHgC4XtQulu4dqEtEF4UIdBJDVZZC6OQz69sXVjO14RxVavJFJcjPqzieKOb5asco3I8gveLFNp1YWS7us6pAKutS0wzUnGDMsaW7mKok28gd/SIhQPHThx9+mBfKJ7W0u+Bf//qX/e53v7MHH3wwb3q/fv3ylNm9997bVq1aZfPnz99mqmmPJaap4vyq/AyEQQdAdIPSrnrD81YGhSNpz12YWpJiRa8PZBcVNWAmVIVCjAOT2iH0QonuaohOtW6h+Xzri1CPdcj1JIKIVelCy0uzYLmjWOgEoXHFQYZJqHsdiITSGNtQA8V6cA9Hm6GAiQgn9pQXw8VE1nzhfTl3mEgYDHsZsneisD19ENH3oBpWx7QCoWocQ+8QKoaiNpXAIpBwBiBchVbIq6RJTflQxyuxyt1rgLGz0BxbmUjCdQ2QE4tQz2cQy7vc8H4b4lGnIBIpIJS/+TxVVFRIOab9+vWzWCxmq1evzpu+evVqq6qqwmWqqqq2On/L/69evdoGDRqUNw/lit59993Wt29fiWyOHz/ennnmmTbnKxQ9lpjWbkhYvHkLoyyvcMkFpY7VApGoaXIfzFKohm6Al0QTEGLq/JTykV+sygdCG6EcHqjKt/r17jSy/Cgqc6dthGXL3GRxyqdCFRXznXzLQjU3fTWgippxt0mKGXUSIkJEJBm7HFHu5OfuhwON6f5bgvJJm5vcBZPFWu5oIgUkFFRPIo1RJJfU0UmsShdeatgdikIX4gsSi7DIT5WWVT1WsXUr3LNwLzqgez2Ii4DYqQzPe1xUIAmFqqHqR26QjkawbxEo9CESGvEP2GaFt2Sm8VBVOFUyTFA7f9HlwpakBbQ9/YLnmKpIJpM2ZswYW7JkiU2dOtXMNhc/LVmyxM455xxcZsKECbZkyRKbOXNmdtozzzxjEyZMMDOzESNGWFVVlS1ZsiRLRGtqauzll1+2M888M29dnufZ3XffbdOmTbOEUMz25ptv5pHdjka7ielzzz3Xqu/Vj370Izv99NMD71RnIJOOWCaHKFLxE73neiXdh5WKnwi9ktpDVpZwSZ1rsA8kj4KFZPlESejkWUrzNcB8ojkzklAqdIL1YU9xZR7aN1KpAlSMp9dAXJ32T+5RD6TTtwkMqcPthRZSFAVsovW502KgmNK3DymVqnobFdoQojoYK/wljO1CCWLbT4LqGmDYWtVX/KT6X9L+BiGh9CGBCqRIKFSS6AepfjSWqJX1BNg3VELR75UeRkqXEdTmetim6lSgtl9V/U7VgjAiN2pxVu6g1YNaks6aNcumT59uY8eOtXHjxtmCBQustrY2W4g0bdo0GzJkSDZP9fzzz7cDDzzQbrjhBpsyZYrdf//99uqrr9qdd965eRciEZs5c6Z9//vft5EjR2btogYPHpwlvy149tlnbfny5XbKKac4+/Wzn/3Mksmk7bnnnmZm9vDDD9tdd91lP/nJT9p9jCraTUy/9rWv2XnnnWfXXnttllmvWbPGZsyYYX/84x+/MMQ0mcpYPLVl8Ghudm8kyictpfEQxqBSai4CKheF8gl+IkrdoWIRUkcpvENhJrDDQAYDyzbDC4EGU1XlgPxUvypFVk7ouwgkz6OXa73W5YemUd9yleh4DbQN9zj8lzHTACH1ZshNhGNtanCnkbLa1OBef+QMEEWIFVOPcnox03HA+mCzfqD5u4gg3bDoWtM9wYquFvKWrZv8yxFpUnNxRYN9VEzVsDLBT2CE1B55Xa1No31TUwMAWJUPxJQ+1p1l1XQn8naFyJV8rKqPq2oX1QgiSaoNG6Ye5GN63HHH2aeffmqXX365VVdX2+jRo+2pp57KFi+tXLnSojn37r777muLFy+2yy67zC699FIbOXKkPfroo1kPUzOziy++2Gpra+20006zdevW2cSJE+2pp56yoqJ8/vDTn/7U9t13X9t5551x366++mr74IMPLB6P284772wPPPCAHXPMMe0+RhXt9jF96aWXbNq0aVZWVmaLFy+25cuX28knn2w77bST/fznP7dhw4Ztq33tELR4k+2/4FcWL97yUPQf6KpeVf3dB2n3Pu7pAmcY61vkPsBVJe4gNLDYnVYcd31M45H8F10q5s6TisFDToopqJ4eVdPQsnU17rQGIKZU/FRLaisM9LCsXw0hYppZ6x4DhfIpnxQ9UEViisVPIjHNADEtFI117ouEQvSkjqrENAJNIug9T8S0qZYIN6nXQEzL8o9NbWqghqgxHQOACieQtQ4npn71XiyaUluSyjZQtG903lViqnhqqrmeRJrUdqFqLqZ6TijkL75qJWJKoH2j41eJqZpCQOIHgdTPNohpzYY667XzxV3rY7ruN1ZR0ba3eP6ytVbZ66gu2e//BLRbMd13333tzTfftDPOOMP22msvy2QydvXVV9vFF1+sd7boBmhujpjlqKSYigTvEVRR4fklJbQhDfl52HAFciB9ofx4VKxwbCtU0gJUQmEaheMpXEShpjogv6IRv8N+1BxOUjhhvgyqrdAh6XMosBKT/7EQhxRdUEyd+YgMUWclMZ8UW5ICPCjgo3pzqtRXl6Xj8EPNp1RbiJJTAxYJQYga51ND/jDI0P3pVyojVCBJCifm8IrNCVQFmu5/MUWHlb+2mynICKKEqoVeYhhcfj/616eqw7RvYrtUmeh3dFU+kdXcZbtD/5+ItcMQOGeZEAWjoOKnf/zjH/bqq6/adtttZ6tWrbJly5bZpk2brLS0fV8VXYl43MvLK21scB9MIqEbQG0qAqudBFQT9i1yV/hpvXsJBha7g7U/TJ+GAp4YfbkSCYX5Iqlyd1G1BzIN/hSOJxUhKhr2+9eF1lDaujLUGpVewvQCpzZfQGqaP4frU6yFd+h0+qN05CJBUBVJCqnHYRoVWHHzlsItpDgv1rfPpJhCwZXURcqMiSRuQ1MlMYROX7oE2mf/BLHQS+0QJTenkEmNGN5Xcxv9kCvcxda1aktOgJzvq6YV+KEa7FNKBZJLWJbuTcxP1aIS/0kO6Z7nyWp37jIhCke7iekPfvADmzt3rp122mk2f/58e++99+ykk06y3Xff3e67775sRVh3RyaT/3IvAjLY0Og+XeW9iDS660/FQKmD+aggCu1sfDmmpKp6nivdUvU+VeV79Rvc+VRllfJJKQxE6ii2vXMnOQMsKTxUNALAUCZcHAr5Z6CAichKlCKUQByaarQcKn8hEqmelDtKRBLD8fCJT8VPBBTCoNCJSLKciuV/+ZHXp1rARFCr/AFI6kS1MQJJ6wpJpA8JAx6F6xIVU9wuDXaojontLInAttczsr1Q7ZIC2EUh1Kp0BUG8XQmq/ZTc5YsIMRVxgctL7nunG0RhPctIFo7+ZUIUjnYT05tvvtkeffRRO/zww83MbNddd7VXXnnFLr30UjvooIOsgfINuyEymUgeMW2EfLo+A92bax28cEvhhVsHxVRkDdVMAzNa9OUPklEodEK7KGpJShX4NLh44kBHygJ8bXt0b9Dgv8kN+fvDiuRZqlbRR8WqfKNoJIRZ0+vdAZeM40mBQxGFQtn+Q6OW1UhW3Q2kSfUkxRRSGYgQUZvSIEotKqb+1A3i83T9g7RLpTQQwfx+84ywL0TqxII9aV0AlXCqubhyyLdZVWAFVVL1GCUggetYsoMV+NSmVDVXV3xMsZWnSC6Vbba2PpJCm0FZpWOliB5F4HLRDZRH79//a+8yIQpHu4npX//6V+vXr1/etEQiYfPnz7cjjzyyw3ZsWyMa9Sya81IkxZTQNwWepXAWK0AJJRJK4VKCv9iJDPYznrsjUfL7pMGACp2IwBIon5RyzGjwI1ILxNRPOjEnTjRdx5d6ENULiATlWBIJi6agSGiTQibgI0dWKTUSSkVSamgYlwWlNoKh/La3gZZH1KkpSP94AKno9LFC0BXYtu9jtZBKLdaSw9G0PiImQWya/AiSAqAqi0G6PKGySDm7EB1TGgXQfqjHoKqZhdp2tQb6uqR8UgqZdDO7KM/LmNfOzojtnT9EPtpNTP2kNBcHHnhgoJ3pTGzuMrZlAGgG1eeTGvf09IcqeoY74BQDSVjb4G5jUIl7Uzek83M2i2JuCATD9lT1mIGQPxHTpJsz7NWsdudrhKImNbxX4Fd+pAiuA4TKKZ9ULZyJNIEqTV6p8KKnHNA4kFAiekTgiGA6+5aBQhrIf86IIXoC7RsVNcWhaxQXernbYMXY92EC0YFopUuGMpugSUKAsD0Sc1EJRZKsmOlbO5RPZV1Qga/uBxIY9bkmsqoQLLWBgWpgT6dSJboBwuUFFz8RVHKJ69eItAx1WbHewXILe+GDubMRKqadj/+gFOUQIUKECBEiRIgQX2T02Jak/lA+CnzQDYpAH/QkcFB4PwoKFMGvhlKOKYJCIVTAVIAh8FYBOWBYlV8HSi2lBmzMTyvgghOt7zqpKOlPXdUXjfPRP9KdFsMwGBRYoY8phWTzp1FhkqqOUtheXR+pozGYj/JOyf+bUhnQLktoD0rqKIEKeEhFV0Lqm+cT7ztxX3Dw8NtFgSuF6lkq51gGMbGX208WqPypKh0W4Wjdq/CeoO5NqhIqtyRVzjslp8NsQTo6qVEv9VqgywttV1tdZ2FzVX57Q/mhYhoEPZaYbliftFjDltASGexXguk4gewJabyhgqh+EJKOCH1uKMc0YhAqA1spBAwaXiMY3auWJ2WusbP38afufDTQobepbxvoE6lVIFNlPV0wtJXC9ntQle/OhSSBCByRv4iP1em1wVqOqYnhfdUDNU6+3pDxESNbJQqD+c8xEQmx0QGlPKjJDRiOh4uhkkR0EkjD/eS3KWuklsSwH+jPqRa6ANSwvVgQiWFlhZiqRArWhV2ZyJ8Z1hcJUlnfkW4DKkGUPVup85O4v4oXrRmmWrXp/EL5y52MMJTf+eixxLR3v3qL5/hKUlU+gdTRGij0GFrqPnDFoMCS6T5V1/t9Sz2xaIqsocyAcKlN1VGBEKtmg3xt00tNgejPSJ1/VAWOIKto0KaTFEhnXWJ1PJFcspUiFZXQWKflsdJVxUp9UEex+Mn/4gQF1aOXoeg7iqBrCIVOSEKx6QJ8cJDyCfeiv7BP3Q8s4FO9SNUCG9XyTbUf8o8nQfJVRcKN0RylXWhrUMdJpfOVui6ZNBfeJED2VEWXF/Ed080Q2kV1PnosMd1UG7d4ZstgRGH7z2vJJN8lXFXF7sNK1lA1je76qCVpA7x0k74XLPmY+i2lzMyi1CGKIpQdXUWInoViRa+iyjSCn2gpqTR0TtyXQTOooxTKJ6ACK1ZIFyqiYKgc38saMaFQvkKQzVoppiLiCM8E3XbkAesnXZ6YtqE6NVBBEBrsq8VPtA2xwAhJrbIc2VuVQToK2luJkZCO9OJsDcr6xG0ikeyM48J2nqJS7XR+gvWrxxCkqItcFFSHALV1KXWDyh0UtrWnrYCwKr/z0WOJaTKZsXhOzmdxifsQloKFFLUaJVGCDfYhXExjVYFRAKzKbwDLJ6jAx7A9mvOLFZ0d7BXoEN0kEAkKvZNxPnqgar6jnqiiUtiauiGlQTEnQugndapPKIEq6wkNoI5SKJ98UWlcVjtE0YeTU0mPOaHkHSnY8RinbUSBSGagiUM0SR9/GglBEcmd5BwHtreFDzNykSC1tVPySQvNd5QN3OH8gkqLjXED5ASiDZT6YZ4UOimpanZHR59UD1QC7QvVO+CXac52g1hWdRDCUH7no+s/R0KECBEiRIgQIUKEsB6smDY3R8ygGKkt1EPYsokUGMjto2mUd0rzOVX5UP5CofwYKheg8FAuKn3NohG/WDiQorCi+PXuDyGto7ZMWpg1QtX2a0HixBxD8DYFtS1RirqXOx/E6ahbk19F5dQsUunE4ipRRaVlUUUVUw3UMLjTwYn6wovqKFW9q1X5pKIS5Kp88tRF8/z8aRE6ftExghVOsaiJkKSiS1hWNbH3g0LKoCJSnqgKOeRPvsOqUkm97Atteyo3BKBUHlFtxfNOCq9Y/MRt7rY+XxjK75HoscQ0mcpYPLXlgWpscAcDsc00Fj9FoaUNhfI3Nrnb7Q85q85+QCu/KJYHU8hL7AZFg4aarK6Gi2hghmlerdDqttAcCDOuGAV/IwrlU05ghngzhN8VEkqgSni1tz2OmfAiUUz9W4Mc8RXN7v3nmAz2Za8CMr+H/AG1y5UHthz0AYPhfeJSNPD4CSzl8FK3LTqXdM6pmBKr7cULqxIuLJzyLSuGlDGkTmQ1QGoABxlVEiJWzfuPt9COWWZ6YVoQdHTaVjdDGMrvfPRYYtrYELVMjkrQt5+rBJLYQM9gb8qTA/RNuSNCGhQoIpgxn29pBAY5UlExoUpVQurWwfpU/0DxwUSrEVLD8o+fyAAlJ1KOHS5LPpnqMYidn+gbgVBoHQZ1W8JKfbgpiAxTC10ixKS2BkmBUzokyXZMlBMKHaKIhKIHKCmVtKx47+ArXVFbIcdaPQZUUWlgIyVUhZoXqRBYVfWjHFOxsh79SYnUEkkMkk9LOab+baANlhgdwMFE7UAl0gMk17qhnYPcXNTu0JI0rMrvdPRcYtoYyyOmql1UDF7CbLDvzre6zh3otq9wpbUNje5NHU/lh4uJhMahAj9G8lgCKkap+Km4lztt4xp3Ghni00vy8xp3PhW+AVY1PyfIoXxaFl7qGagvo3cJteSMNKstQ/Pnw3c8FCFhf3qRIFKInrYhC1BwXElRlXWq8sVqe+xjL1oooZWTSEKjKnFGxdjdPf8HFt3DBFkxJSWUwBVsMJ/qn0k3im9fVCIVxE9UTSmS3QuIJMI9S2NnoesPgiAfEh2N3AGqG9hJbTbYb6diGhrsB0KPJabppmie+kEK12drXcWgb5FLJD+pd2/CUnjQB0Llfy286EtB+WrM5BOnVMztY9+cgcrimOt0HgGyGolBRW8zxKMDhPIxj6uy3N3u5+vd+Xz5U9ESUEfFfELKCcVlxRciEc6omrOo5or6EKSIOpoCdZRcBKArUwLSG8jbNNVbcznA/DxQJR0iCtfGMaG3VlRPAOZ6kuWXqECidRORZNoXIMQW9fmYQj4tHgPcKNhtCvMTKZQvqqhyJbnwjHWwPynmp9LC6DuqfRCg2qiG5P3bDaI+qlBzVvG6Ut4pHCu9O74A3qahYtr56F53QIgQIUKECBEiRIgeix6rmCaS6bziJ8rPK6twlTUSGyrEYlA1+kJpAFjY5AOF8iNiSMFLwxdzI8So6QtXaSHawcCwKPmOUj4pKUtYcKLlE6JShcUpWlg9CvqNUthEeaKx4sLzH7GFKHWQLCMvUjifWHTkggqb/CoihaPxuNSHjtwb5Mp6mE9VzMEAH9uN+ou/8P7XOlUhgoRoSVlDH0+xmKhQFTUI1K5MYk48Aivahdx22qZqdK+qtGo+qapmkopK+AJUr4fFT52PHktM/VX5ySSE3oAgVkDrxs+gxeN2pe5gQtZQNAY3AAkpjeeH7qnzE4XyYxHIJ1UTypMl7jSs3tcGK69B61GP4SKhyh9JqJiLqr7AqZiKSFi6rvDcTiKh/nxPGs/JYJ/3g+mgAjwGkXA2N0IrVDfThAmW7zbGcLRK1sTQO0Lt6EX7hz3aNesPfxqAXNSk5nzIXX5Ey6MgZNVvXSVWA0YiYi4qGt0XbjXF56TwcVJaLkg+rdw1Sv2ogcYmSep8JYbtc3OW452Q09oGQruozkePJaabNuW3JP1sjfsgJVPuQ1EDRKcv5Oytb3QfuDJ4cZQCWa1MkldqviqZhNxRBObwwWWnB4kUUxyEYADb6C4bKXb32duwEbYhvOioJaXYVhKBCqdWrEJApRIUU7X3vJ/UYRcpsh2E+aiynqyhqNCJKCemiVFBIHm7ijZdEkQfT1a9gaxSdyVQQum+w+I/cAigNqVKgZW6TVT9CvUTNWvl2YRrSFEU2gZM8+eiY8V8sTtee7Uw5gQhnAQ1d1a10CqDj/963we8bGUFUPeD7h31SxqL34J0Est5TqhgrJPhWfsV0FAvDYYeS0zTDRGznC/s4mL3pUEFUcRLaoEQVAFvjINSRa1LN8B7ozeQXwm0GDEYIqupMlgfsRAgq9Ar2vtsnTufmtjve9FxZT20GsVCEhiEwZ80BttIwzbwypDlEZCaREarBvbbPhG5VEF+qqRmUlU+96OnVqABrGYoNcJ/zdT+9BRSB8IZLaeXMBwDbZf2V9wXhGieL4GUOyrgUc3Z1SIp6hVPBZHgEEJ+pMo2I6VA8oKQcNUkXiVhNNYpanCQtpyorJI6GoAKqIopRuraMN2nxg+djQIU0y9CikJ3Rlj89G9Eo570y2TM+RXF3B+hIR1xfhuaYs4vEfWcX8T3v3gk6fwyXrPz8+B/lihyfpFowvmZl3F/6Wb319Tk/uhEASKJhPOz5rT7c9bnOb9IacL5IWjZaMT5ZTY1Oz9EU9r9RSPOz2todn54TqLuL16c/4sVR9xf3HN+Fos4v0gi6vwSpRHnR5ffYlHnF0lEnB+eEzrvqZj7i0Wcn3uO3OtFP6++2flFiuLOj641nSfaBsFryji/SEnC+dH1wZ/vXoqk4u4vEXN+dM7VZ4Lng+c6HnN/0aj7o/lgfZFUMv8Xizk/BI0byYT7U+ejY6Djp/lofbSsMk6q61JB6yPQQETjPx0/zRdPuj/aRqY559cdFNPC/lcIFi5caMOHD7eioiIbP368vfLKK1ud/6GHHrKdd97ZioqKbLfddrMnn3wyf989zy6//HIbNGiQFRcX26RJk+zdd9/Nm2f48OEWiUTyfj/4wQ/y5vnLX/5i+++/vxUVFdnQoUPt+uuvL+j4VPRYxTSa8SyaM/jW1roPZ0Uv90uQFFPKMaXwPoFC+U1kzu4LqzRBa6HieIW7nJg7iGEbUlHJA5XyTmkTmGMHioagLGBuHhSNoFm5WDiFYWZSQkGVxf2Dm4csjogAR3wDnTzsifmfuG8pLbytqpccGidfWPeeiPXzqWGUyqEqkrBsVPQFRXWU1DvIWZbPnZIugjmB4vEHsEYLkrNYcMtQNZ+SzOrVZf3hc7NW7JIKLGBqbX2FtiRV90NNPSAVlVQ/eicQ6H1Cyiq9T3IV01h3IKadYxf1wAMP2KxZs2zRokU2fvx4W7BggU2ePNmWLVtmAwYMcOZ/6aWX7IQTTrB58+bZkUceaYsXL7apU6fa66+/brvuuquZmV1//fV2yy232M9+9jMbMWKEzZkzxyZPnmxvv/22FRVtOfdXXXWVnXrqqdl/l5dvsXGsqamxww47zCZNmmSLFi2yv/71r/ad73zHevXqZaeddlq7j1NBt1BM2/uV0IL777/fIpGITZ06ddvuYIgQIUKECBEixDbCjTfeaKeeeqrNmDHDRo0aZYsWLbKSkhK76667cP6bb77Zvva1r9lFF11ku+yyi1199dW211572W233WZmm9XSBQsW2GWXXWb/9V//Zbvvvrv9/Oc/t1WrVtmjjz6at67y8nKrqqrK/kpLtxRb/+IXv7DGxka766677Ctf+Yodf/zxdt5559mNN964zc5Flyum7f1KaMGKFSvswgsvtP3337+g7aYq0xYv3vI1Vloqdv4ADILe9twNyp1W2+xOHAzm8Rkvf1oM7Dj885gZF1s3uAVHXj2Y2hNIHaWv/k2u15BXBwUR9EVPhRO+bWCBiLsUK4agXGEbTPGrV10fqXLNn0PeHX0u+pU1OC4qVkqUaNoq5axSERYBw9lUJETG+aSiCgVBmXpQvUmlIqN7sGiSq/JVSyZSPekeay3dpI1l0VeB3Baw1aiaY6kW9dCyVMAlVtf7hTVsyUk2W+QsAK84VCTF/E+1X7CqQCu2T5QnSy8YuoUxJUM8BjVPklRPyidVi6macsb/JlCyOxmd0fmpsbHRXnvtNZs9e3Z2WjQatUmTJtnSpUtxmaVLl9qsWbPypk2ePDlLOpcvX27V1dU2adKk7N8rKytt/PjxtnTpUjv++OOz03/wgx/Y1VdfbV/60pfsxBNPtAsuuMDi/1blly5dagcccIAlc565yZMn23XXXWeff/659e7du13HqqDLiWnuV4KZ2aJFi+yJJ56wu+66yy655BJcJp1O2ze/+U278sor7cUXX7R169a1e7sNdXFr9rYcfl2deypKytyHKwGhBfEdhIVONH6lYUBICj6mGD5oBpIHRU0RCLN4DRtgI4UndUdSQAhUsuobOD0Ivan+nEgagQyl1xc+KBJZy0DHqRiY4Datg0IsHxWhblNUrJSBNJMoFGpTQRQtGysWvT1FWyX2lAUfz0Jb0IotRJFGqN6mqj8nLUvpJ9QH3XccaJelhvKR6IiV1USkcH3uTYa5oUpIWiXSakidbuEgRU1qy1A8x/Ty8G0XU5sCWF4RQSQrvyKo4KVwPE1TQUJHd7OLChDKr6mpyZueSqUslXKfjTVr1lg6nbaBAwfmTR84cKC98847uI3q6mqcv7q6Ovv3lmmtzWNmdt5559lee+1lffr0sZdeeslmz55tH3/8cVYRra6uthEjRjjraPnbfxwxLeQrwWxzPsSAAQPs5JNPthdffLGgbccSmTxTfarKLwEVlT5UyT4T2t3bRmo/Gne3uwlU1CIfqW3y3IEkZpQTJeaEYj4R5d2BhZT6AqsXraaEvCtSmiLwhUBV9FhZ7m5RbnGp5qcS+SFf1DhYTfmXRpWS2nTigKoRLlRMKe8WwDm2pNSAsqp4ytKLmYik2O8ercbEDx21nSlaTaE5Pzkf+CIGcG/ic6OSIYKaT1pCBAaiCKSOKn6fSDjdSXILTdVCiZwFCJSfqprzE/zzBbmGKujcqSIE5Z0GcRLI3W43qG4PopgOHTo0b/rcuXPtiiuu6Khd6xDkqq677767JZNJO/30023evHlIojsDXUpMC/lK+OMf/2g//elP7c0335S20dDQYA05X4MtXzDxeD4xXb/OvQBU/LQBvE37wviFkRbPfVk1QqFTGZj4N3v5+5KMaD6mGYNuM02gUqogspoEwrnOVVvRBkYtThAGOlIk0e+xSSw4oTBrLahDVOji7+3eCog4NdW4yzo+o2odCREksnIKkMpAhBgVU1gUr49Q/IO94sUiNFUxVS2a0H4KBnT+gIFzp+wLPTdEYDAMDudctYtSuwupxTnlpe40/wcs7YdoRyV/NBNUv0+RmOM5URobUGoTEV8VFGan64pt6URlOci4Ln78dhaCKKYffvihVVRsKUpujej169fPYrGYrV69Om/66tWrraqqCpepqqra6vwt/7969WobNGhQ3jyjR49udd/Hjx9vzc3NtmLFCttpp51a3U7uNjoaXR7Kbw82bNhgJ510kv34xz+2fv36ScvMmzfPrrzySmd6Y2PMMjlqDamjjQ3gOwmh/Hro1FMPL9f+oEA1pKEqGQhsLJI/WEXEurUo5U5RThCY6UdiQLhoI/RCAOPoCIXfqSpfCMnJHX3gpaa2LsX5cH3avpASRjmmZEQvCQdwz+F+wHERqcuIBvPsRarl3dF5J7LqtCQVuzxxIwbNn1T1QMUKfCK/ePxAaiEHNrMxn6xF6P2GXdSISIrKHZEL8iIls3v4CMWqfIqi+Ak2HReNOaSOqmFwlVypLUnVSJCSn0okXD0GUjOJIKvKJJFV9LaGbWCOKZHkOP93F2Fr7mlbW8bMrKKiIo+YtoZkMmljxoyxJUuWZIu5M5mMLVmyxM455xxcZsKECbZkyRKbOXNmdtozzzxjEyZMMDOzESNGWFVVlS1ZsiRLRGtqauzll1+2M888s9V9efPNNy0ajWZrfCZMmGDf+973rKmpyRL/foafeeYZ22mnnbZJGN+si4lpe78S3n//fVuxYoUdddRR2WmZfz+M8Xjcli1bZjvssEPeMrNnz86Tqmtqahx5PUSIECFChAgRoqswa9Ysmz59uo0dO9bGjRtnCxYssNra2mz9zbRp02zIkCE2b948MzM7//zz7cADD7QbbrjBpkyZYvfff7+9+uqrduedd5qZWSQSsZkzZ9r3v/99GzlyZNYuavDgwVnyu3TpUnv55Zft4IMPtvLyclu6dKldcMEF9q1vfStLOk888US78sor7eSTT7bvfve79tZbb9nNN99sN9100zY7F11KTNv7lbDzzjvbX//617xpl112mW3YsMFuvvlmJJytJRt76YhlcpTOKFS0Uucn+ihNQOGIaO1oA4vdL/8GUGD9oYTGjFv1XhKvdDeAiY3kdweFSRs/deejNqUEaEmKX+oU3hf8+FAJJEVKLGCKlMK+1cKMYjgW0wpAWaNCJFZlhfA2ihnkneruB3mHogJN81EIHVIZKPyOUNRQylelECBNE9vKojpKaqtYbY+5rZTyAI0XHBVVzDGW8x9V1U/MHZT71hfaMhQVSVDfqAAAFUixyxNFoIKotwpQCVc9e9soLtrafNi9SXt3yN0FvwBIexFLQxSzrWXai+OOO84+/fRTu/zyy626utpGjx5tTz31VDbVceXKlRbNuSf33XdfW7x4sV122WV26aWX2siRI+3RRx/NepiamV188cVWW1trp512mq1bt84mTpxoTz31VNbDNJVK2f33329XXHGFNTQ02IgRI+yCCy7IE/MqKyvt6aeftrPPPtvGjBlj/fr1s8svv3ybeZiamUW89mb1djAeeOABmz59uv3oRz/KfiU8+OCD9s4779jAgQOdrwQ/vv3tb9u6descX67WUFNTY5WVlbbX1f9rsaIt+U0DBrksZOAgl1xtV+k+mF8qdU8h8Zxh5e4AVppwB85iqN7v7atoj0GopCjmVtsnY25IncL7kZrVzjQvDQNufY07rQ4YHL0QKVcKXnTeBlifz36KXt6ZNS5ZV21b/KHS1oAhfzJOp2WB1FFVOnaEUvIuiTTBiwTDzGJhDpvEQ1ELkFAkq1TEA/CTOkwpgJ7taqoAkXVPTY0Q0wDkKn8isEqLV8V6qLVplJ9KgI9LJKFqIRLBH/JWl6MPX3r+gzQYIKj5lIUuS/tGZLARxlxalggihuPFpisUbqc6hiS0jG2jKr9mQ5312nGmrV+/XgqJdyRauMJf/rXAyiu0mo4WbKips92365r9/k9Al3/CtPcrocPQ6JnlqKSNje7gtwm6QW0odh/+z+i5hOe8P3hFxuHYEpG2SYg/59TM7Q5lZpaGXuwxsveAoiYUWzEhPoB1jfoC850nUpqilaCMk8eo2JVJ9UBFEPkj8iPui7OsmvSEfdc1goQFXGp3JVJq4frIxNmvQIqqZ6HFVWZ68RNCJaF0rYsFFVEloYWqdK2ALN8wd5Kmqcqff5pamEXrUp0K5HzSAES30Ep61WNUdSBQofqO4vuEPpLJlQXIau76SHntZGS8CNZ9tLVMiMLR5cTUzOycc85pNcH3+eef3+qy99xzT0HbzGylz3ULKLxPSijJ9uVQWb+uwV24LOE+rMXQprQhnf8FGo9CgQQY7NN8WBBBX7g0MNE0+noXK3q9Wgj5Kx8i9LKiIiSxsh6JGRSrRcDySw1bo/1Qg6bK+Yk4kjcKlcsEVgwNi9vFdAFQglGphcPwP6se+bGJ+TMdTTjlbajhd6WwhSrBCXJ1tEj0MGwtvkbI7F4l2A5E83u5+ClAQRh2+KScL9GSyb+sWnBE4zCBiCQRRNqGul2xTXV3sINqCxlP/pbNWyZE4egWxLQrkCjOWLxoy0OxaSO8SPu5ZO1z0UuY3l+qwX4d+Zim2r7TmzLu/iaj8AKjr1k1vEPzEUlAWxU3ZylCoXwiq/4qf7Kjoi4/1ItetBAiENFVl0UgWWlb5ZLJFVblw/USOx8h4VYtmShEjaHGtv1I5esldqXC8Lno8iADUyhE+51C7YE62jhe7XKE5E+cRiFpP2SLpgAfa3RcRMzRAihAaLzRN46rYoAqLpAri0pCg3R0IlAaQDdQSXMRKqadj+5lGBYiRIgQIUKECBGix6LHKqZNDTHL5MQMe/VxvzYbG1zeXky2oOThTpEcSA0gdbQCCqIUg994BPJE4WuW6t1ovkDedqRKkKKh9qP2rw/mITUvsw7UF1LkxNaV7Y7p5G4DfTEhDYDkdr+yRiol5YRSNyixo5W6Dc6dFP1DxfNJrgHO+lV1lJwVqPMTpYaIHqh4Xek8pcS8UP+xodIeQGdQ8xMpHB2kOKnQqnzMa6fCHBqb2u4s1yqgwM7IizkJKiqGt+mc+BRNUhDVanu6hkEUSdqGko5gVlhUrhtU8gfxMQ1RGLr+qncRolEvL4eU2i/GIU9UrF8wKjaOAzElskrT/Ib6HlZRupOwAp8GZhg0vSaocie7KBpw1b7VBHqZ1Ps+HOhFQmbdZBfUBPtLuY6UZkD2VgDOJ4XcVjW300eSZDP5hPiIB8lZVUm9aL+EhNDXZ11NASCHAzUNQnU5oMATug2oRTKqEbuzfjHHkkiTCrXASLWaotC4QlbVJgFk70TnXA1HY9ckWJ/6UY/z+falo8mZmotKUEmomi5A5zP3fUL5sJ2MzrKLCrEFPZaY+qvyKce0vNx9KMgWj4jpOnieKpLuzZoG9XITqKilPpJMVflRqBpJe9CLXe3KgUpogKKDICqH/2UF6itWYBOg2xaRBvIOxcIkkZip+ZkEv3URr0sjueo21UIftFqi84nEFFZIBNufi4YdqEhFDzDE4fGLdlGYZC7uCz0nCllTc0IB2JUpiIUSEUfV29O/XSL0amclQkfbJQVZluBfltZPuZltWC9tdT4V1JKa9kXNWf0CIGOYMdzmMiEKR48lpv6q/GYgg2SwT6B3EL37MboN22iGr62ITw6lQidCIgpvflEdiBS7hv0evejqwNuUEvHV6l1oBeuQDnzhqiqKCNEaCZU1Uv0CvOjdlpwQKqb7VVVCCaQ2U2Gx2OI0ktCUmmg5zOd3JVBM6GE5Mwt0bdSPH/RApdsfPUVB0fQTuCA2QEGWJaAqK4bo8UO3wJSEINZYaoGRqhiq26BpNHYq26T9JcVRPS7crlj8FCQNLJfAxzt4TC8AGSug+AnNFkOo6LHE1I+iYjBshxc9Kab0ruKQv7s+1WA/6lNIKSeUTPdJRcVnhuxCNq5xp6kktI7SACBsp6oc/vnUlwERkySojZtEk3wgRGpYPb0ByBSQMPRK9W9DDQurLciAhLKZPuWsav6sqDaLuZ3uNigESKo3pQXQeRKVcNF3Fj1b8Zq5k/je9p0nldCqPeBxPpHoqbmt6naVj1B13CAlGCNBYh4nQT0uVUX0K6tqmkEQK6cglfVEfimUjylfNMbkLBtkvzoIYY5p56Prr3qIECFChAgRIkSIENaDFdNoPD81hsz0cZooSlHeKX1FUVU+KaZuVX7HdnTBr17KHVJ98UhtIMW00IIIankKoXxUzMizkmRvUscoT5A6RMHexagzlRgGz2xo9M1TeEcjAu6H6pVKYWs1bQHVNrj/heIvta2q2seegPNhkRjcO2qXM0VFVIuL4PxiPqlcWR9g3FFyxwk0HpSUutOCVK9jR7sABVE0JhJU9VZZTg2fU55oEBVVVWXpfULIPY5ukJcaFj91PnosMY1vbLZ4bnU2tLNtgi4/BArvU2SYG7+4L/UYTGv0tXMrjrsDepPnkrUiokiqlQcNTBTyp0p9GqyKxNaFiom3GMpX20B2NJAQiYbtRH6ckD+18qQQOOVrAnAYFW21MnCzR8sLtAEyLljynzvMV1XTFhopHwfcAVQTf5qvmDquiaROMdPH3GGVrIothGlZtU1nlIqTRPcOv2F9kdjlSiWhao6lmp9JY6IKpXWnShDVHFa1mEol5rQNuhZqw5ZuBq+AUD7UNIdoB7r/XbGNkIlELJMzoFI+aSLhPkjUkpSs7ei9TIVOKbCpUjoXUvtR6vJEtlIe5J3KPqZBvmBJ+QBQP26voTDbEOo7nqlxCTz5ThIoxxRB1kXkC0qemorypzoQAFQVGZeFabE+rjqExxUATvGXaD2VWevmOkeoSIo+JGhHkBAGGEZV5dMPIK9kZea32dq8frX9qKj6ErDaXjxW/1iE6hsRRLHQU1Up1Y91tcpdrV73K5pBrKfoGNChSs07pnxfsulTrdYAuee4W+SYhp2fOhs9l5gmopbOeRllxE8i9cuJRLmGtHuz0jSqfPZvlwqdiKymPXfQjNNlV61G0hBCz8D6iMCpA53ypU4tCWs2wjYL60Vv1kplNZCQzHr3nETBP5T6u1N4nxRISeWlKnpSPQOklrNiCv6sIlmTfVz9LzXRjipaCW1widR3cDOFINZNSv/4SEQ02FdVWvpolIuaRC9WuiVSoDb61TYah4jkIUESiaRKYAlqKJ/ImuLTqdpRqfurXlf0HRX3hYAkmcSP7uZj2v6hoBOCcv/R6LHEtHhDk8Ubt9z0mV7uAE45prUiZ9gA43z/Igrbu/PVgc9mZTJ/WQ+0K6rAp2lN5u5cQq1ApfmoKp9eHOhZKr7UOtDiBokp5AlGqFIdlMBoBqrSSYEl8qt+6fhleSC5qHoSuSZiJr6s0LAee6WLHyGiL6yfYERKtG5Tcu4sEXhwG5BRaEcjM716348iyvUT3RtUUqt6iiIxF/ui+5cNQnwI+MEtklU6LplwFWjxROctiJqrpnIR1Gshb+OL0PkpVEw7G11/1bsx6urc00MklN5z9O6nsP2mZvcGLoeOU2nfoEY+phTKJxUVLaSC2KWooBcndGvysMWfUPxEuXmgBCFpJAspxbbJjG8KIjqUY4ptL2Ffapt884gdkyjPhIq1oBuWXPwEIMXUI2WVOiSRyqVsk7xTRUUW95dIOCnB9MFF+ZRY/AP3rEL0qYAJP+iISAYY9lWyqiqVSoFlEE9MFaqaSSkEBDk0Dsv6j0M19ScgudYWlQm3SpLR27qNj4SO9twtAKFdVOej6xM4QoQIESJEiBAhQoSwHqyYNqWi5uWEb9PQbahyqPvFTEVSRXEtRE/V9g3UHhPgt58oirhfms2eu79FkTJp/ZaC+aDaPkLFVPT1TpX6pJhQa1Eo2HDOHIUPKU+OVFoqzKG8TlJHVVshrMonJRS2CyqnVF1PKq2a6wnKhG611XYVvZmZQWicK/rdY/VX+dNy6HDg7gUr3CBc0vWSu5epaStYJEQ50EKuaAALKTn1IEiKgmoX5J/PX6Vuxoqh2p9d7Zqkvh3lVIMC51PdBoIUq6p5rHQI6nbVXOGOjtQFRJhj2vnoscQ0kvHybF6SKZes1Ne5L4MysJVSHVmIhFLnJ2pJSqTWD3/b0s3rcgd1CvlbmoofAoSQCJsoF1XM42poaHsesT83Eg6owcB8UiJX7qJyP3q185G/EEu1o0ISjuFtyrsVX6SiDRbFt6K9KP0C/Gj9Iz3lzlJaAF0HOk94H7qT5P7xSEI1Sx4sbPITQjUPW32+glgDqfMVWnSjEtogBHFbV9a3Np9KCJX9COJFqo7r6rUg0P615SQQ5pj2SHT9Ve8iJBsyFs8xZG9Mu4SDckzrYdpnMZfUkcBVBXyQ8k7JdN+vmNanXRucipi7AarAbs4AWY3DzoHq6UHOqtySrgSm0QsWlM9IaT5z9NZvcJdTVVQCkSax1aZa+S+TVSq68ZEOzIkkhRPIJe5bg+hPCVCKlcwMbbCIhJIC68xDJJTmU2216N5R215SbrO6LJFJJX+UlFYivoXaUbW2DVKzlMp6M53UKC05g3w0B1mfPJ9WnCcVLKGqKKrIBPX4VaKLxa+ia0CQRgGdhDDHtPPRY4lpYypqmZxQPhHOsgoIjRe7D2tfeC9VwPugCdIASLwiFdWvmBbH3c4nRDiLYuXONFJWcaADRGLgnxiFsD2F8glUxESDmn8+IpyiYurVQcEVFURRlTf2YwdypYbQsYiJ9kUY6WA5Wop70VPxj0hWRZsqPAYoOkP4zjuT8P+/vXcPr6q68/8/55JzTkhIIgUSYqmoxbuVDkiMl7EXprRaO8x0WrSOomN1xoK/WnQUr2BtxQu2VKQy2ir2+0ixTi2dqg9TxPL4tKa0RX2mVnRqxWKtoToKgSQkOefs3x+UQ85er5B3zg5JTNbb5zwtO2vvvfZtrff6XN4fzfqMlmWVSBL5Q9cwXBeSX9FSFz6earml4xORVkljsp/1jhWXtJoxT1CJj5Id3xeo4x9ZVsPPIgpBjrIvIYplVbWYDjHkS6j85C2m0TBiiWl5a9aS2X3kprMaXLQkiA/xpKRDTXMfch/0eLrn3dVVPDEl4+4qNZNw40Qpe5/aUTlPHKw7QCtULV1Kk38lWFve2eFsCsKlNGhybXOtyGjNhKo8GP9JhJPc7LQvZKCr5A/7Qs+nRGC8Kllz1DhZCnmA2F63rG4P9wQ+FKdQQlzLmMeCCGqMJWbWi1JLYixmrBw8FUpJUqj8xgRZjAmVtT3FKaMcYp7UakBhgDs3VgbFRCgcSb0uRejfTHe9E+Ek0LWFVFOCLCzeycVNlksCVmoiDVgxHAFLUsPxVItp921RQgf6Cd5iOvDwWfkeHh4eHh4eHh5DAoO/HBkk7KxJWyKzb6VH1tHRozVXDllC/w8WuR9wve9oHaVqUGPSxSvVLGoHupvIbd+Ra3W2ocufVuoEsqxiJSlY0ZMrnxKWQiL2Ae1H1aDI6gVJWGThVAXxUe8URdwp7lKsxx4+Hpja41BFCqGWBhSByWTYTq1M1Lt1GEMlKJGI+kZWerQ2kjsySjiCmCWZAQuUcix6/1F3VIyJJERJ9FH1OAU3fdAF3hECjE0xKI0qH09NxsGMSO27Q8tvCGGrqplZoFppCVFiTFV9WkJvSWdqUtoBhE9+GniMWGKaactastsAsLvSfZEo+YnyVypgPKC4U4JamCUbIs6ppNuIyo/StlHJamdbLOwqN9OD5GlQJ7cSufLVhCXHlSmSARDwpzCAGAnxU+Y3Egk1ThTCAMhtDQk7DsHKlF7lCbP+WynhzH02FMcZBcEu9/lIlalkGSQhXtmMySpBlXeiBCY1G57e2cow6aTkJ9gmxl2X5GYtdEUM5Sk13lGsfIRkDRbXAYQUYGhAHsYhVcpIvE/Y5/Cu6kIySoxplOff3+SxO8GO918IU6kYSLmo5cuX2x133GHNzc12wgkn2LJly2z69Ok9tn/kkUfshhtusNdee80mT55st912m51xxhmFvwdBYAsXLrT77rvPtm/fbqeccordc889NnnyZDMze+211+zmm2+2p556ypqbm62+vt7++Z//2a677jpLpVKFNoceeqhz7qamJjvppJNKu9BeMGKJaRhUfjQNElKU00EW0+Z2t+Hho91zJOG8YRJqZhYPJT/lgEiqJUk78y6RzMQh7hS1/WBbJ1kCBauPmdluILVo+QzdZDLm0iRMOqbqZE2TEGmFksYkvSiYgQ4kFMhvmOjGwKquljeVM9rJwonJX1oiEsX2WpriKYWyl+ozpHZqVR41Ux++YVN0R83M4FtH9YowsNpUBBJK36sq+SRrYJYmtRRLlK6dGkuSQK1oHSYCmwK3F0G0NiL5Db+LURKTyGJMZJjUVlT1BpX8EnotDzv4yVF7Ykz7ajHt+3kefvhhmz9/vq1YscIaGhps6dKlNnPmTHv55Zdt/PjxTvtnnnnGzjnnHFu8eLF9+tOftlWrVtmsWbPs2WefteOOO87MzG6//Xa766677MEHH7RDDz3UbrjhBps5c6a9+OKLlslk7KWXXrJ8Pm//8R//YR/84AfthRdesIsvvthaW1ttyZIlRed78skn7dhjjy38+33ve1/fL1JELHCySoY3WlparLq62k65/BFLdpM52V3rfiC1E1yXd93B7rYK+AYPr3Jv63gY+6tS7oBQA4S4KpSpPwaOVRZ3N5bF3YE5FXetQ+lAXEXveltqF3SAnFPL/7nbyLK0o8XdFs7KbieLrOa2l7L+ewJZVskCSZZQscQnCsUL+6qC+GqmOvUDk5XEQgRYHlQlf7TAcM4ZQTtTlTKD/qLuqNoXNXvfycoXr4tKnpKFE8NxIrjjya2slrgMgYgpEimCaFmPkVweivNDYQfoC5I/wUVvBtcLJI9CD/A+wTnV+ylfA4ZtUWGTvidntexst5rD5tmOHTusqgoS6g4g9nKFlc/da6NGix6Vv6JtZ5td8OFL+tTvhoYGO/HEE+3uu+82M7N8Pm8TJ060yy67zBYsWOC0nz17trW2ttpjjz1W2HbSSSfZlClTbMWKFRYEgdXX19sVV1xhV155pZmZ7dixw2pra23lypV29tlnYz/uuOMOu+eee+zVV181s30W0+eee86mTJnSl9tQMkasxTSRzVsisW/gSSbdQagDqkHR3EpQiwaRxZR0TMdlij/+LAy4abDSkI4pxZ3mIP4xQa9HCj5QGnDQKgMTLlklaWLeVTwQExlAi6Gq44halFSpiiZEqpoEmeqjQflhpxjbGna1U1wjWWRJwJ5AGfjUjib1clFTk8i/aqkMP1vRpS4fn0CxzlFIqFr5SekfXr8Wd4ukgUBEUnSryy5vgWAjaYI4UdzXiJjBN6GSKyCrMSLhcDwKF5AQRfyfiCSMzTEx9IJJLXxj9PxVL1r3fVXr9gHEQLjyOzs7bdOmTXbNNdcUtsXjcZsxY4Y1NTXhPk1NTTZ//vyibTNnzrQ1a9aYmdmWLVusubnZZsyYUfh7dXW1NTQ0WFNTU4/EdMeOHTZmzBhn+2c+8xnbvXu3HXHEEXbVVVfZZz7zmb5dZB8wYomph4eHh4eHh8eBQktLsfcvnU5bOu0ujN5++23L5XJWW1tbtL22ttZeeuklPHZzczO2b25uLvx977ae2oTxyiuv2LJly4rc+JWVlXbnnXfaKaecYvF43H74wx/arFmzbM2aNQeMnI5YYtqVSljQzepEAvtVNa6FZ8cOd3WcGeMmK5BxhIxj5SBYfRC48sPi/JSDkoN4pSSs5inGtNLIAqHpDqLOnhqfqlpWQhaioAtuJlqRyPpKBQGEWC/TxfQT5BoXE52wHWmPhveDZTomcCnJRdZDApdYsCAmlt9EqNbQMMjdLz5X2haDCYQrLkWw3qqWVakakhjXSVATYgiiPilmw8OYFXarB1kxYx6gWlZl1zNZ/VSPkaofGmqHFuOMq6JCBQHU+FyMdSVEiTsl9ObeHwJZ+TkrwWL61/+dOHFi0faFCxfaokWL+qNb/Y433njDPvnJT9rnPvc5u/jiiwvbx44dW2SZPfHEE+3Pf/6z3XHHHZ6Y9je6UnELurE7Sn6ibVgNCuYlSoiaWOEej6ShwolOtC0Bgxxl4FPyUyIGE0ScJJoo0QMyS8m90+nG4iJSMNBTVrICGjTVkqQoqwNxkiTi3g7nQHJBbmCtXTg+E+NElWx+M75WKgOrVq+idhguQYsVIrBCnXk6PiowaG5ryqIPckAIpKMZqwGoxJzCRcLEFBNORBc9xo5GyKIHYJIQuoZ7d6vH0hoJi+T2xUWDGKJA51UrSQkyTRj/CoQOCSxV6stp8fQYY0rJSFHKivb2zIaAKz8oQWB/b+bO66+/XhRjStZSsz3kL5FI2LZt24q2b9u2zerq6nCfurq6/bbf+7/btm2zCRMmFLUJx4r++c9/to9+9KN28skn27333tvr9TU0NNi6det6bVcqRiwxLQ/JRbWIMjikd7obllM0L73T4e77/gp3MNnV5e5cVVa8bycMpBUwyVPlJyqrkA3c66fEKQSW1YPBD6tBwWAtSEHFyJpJJJRiR7GcKUw4VNKLkl/UcpZigDJmzYdIDUk5keUSJ1wyQhBpwuSfCFZPVRuNyGr4eHQNat/EmNCYmqxEUBUCKiBmW5nUifioBCkKaSBySZnvqqVL8aKo16VWllKJFFlHabxStU1VkqXokVKcqHZ0Xgyo1lz1fqrXquqnDiKixJhWVVVJyU+pVMqmTp1q69evt1mzZpnZnuSn9evX27x583CfxsZGW79+vV1++eWFbevWrbPGxkYzMzv00EOtrq7O1q9fXyCiLS0ttnHjRrv00ksL+7zxxhv20Y9+1KZOnWoPPPCAxYUF9PPPP19EdvsbI5aY5uMxy3ebFBNggSIS2gkJUZYiS2XpfUsn3K8gXKs3DRaOACqjk8U0F7jEbFTc1TbFQus0CJMLCSarQHV5ZcBCsAtKoYYPlYYymKrLX02IojAA0Q2OcklEkhX3Lrme6fiqhBC66EovtYmgOuuqnJMyV4llRdFFTx9sJGs7fDyqJBmRrvA9UV30qtVTzJhH17BKQkrN/O9vLU56v1RLKN2nThgTVPktvDZFs1cMlYGxmUKv1OeKixARqipB0XmHgMV0oHRM58+fb3PmzLFp06bZ9OnTbenSpdba2moXXnihmZmdf/75dvDBB9vixYvNzOzLX/6ynX766XbnnXfamWeeaatXr7bf/OY3BYtnLBazyy+/3L72ta/Z5MmTC3JR9fX1BfL7xhtv2Ec+8hE75JBDbMmSJfbWW28V+rPX4vrggw9aKpWyD3/4w2Zm9uijj9r9999v3/nOd/p+kSJGLDFNt2ct2W1l1lYFsZhAQoms7mh3P563IMu/AnRMd3W550Bt0xAxbYcJfVSSYlPdQSiTcDVLqV0yBhp4zpYegLFYECtGq3KCo2MJEwnJLBFpBBetTC5IVojc4CnROqhWHArvq1o4R6lanHA88VrlWvGq9RbjOHt35ZOerDw/qLGjKjGXM/8FEmrmWtFUCx+RCxKTJ7JCWeRR4sQJSlysKhyvViCKIliPC2lRxihWokWXwixUkis+fwTlE6gKCQrhNus9jlddgB1ADBQxnT17tr311lt24403WnNzs02ZMsXWrl1bSF7aunVrkTXz5JNPtlWrVtn1119v1157rU2ePNnWrFlT0DA1M7vqqqustbXVLrnkEtu+fbudeuqptnbtWsv8tbrcunXr7JVXXrFXXnnF3v/+9xf1p7uS6M0332x//OMfLZlM2lFHHWUPP/yw/dM//VPfL1LEiNUxPe3/+0GRjmkLCIOOqXU/moMnupa7inL3Yz0Ixu+Jle6tPqTS3ZcspmNCclFhXVMzszIgtBVl7qCZirsDE2mgYnwqjekU79T2rtuOBqvdoFlK7rIwEXmH9hOlp4CYBTtAd1UFEi4ifyKtVxJn1PABlSBi+U21upJIwojU9iPRw0IH6oIj1c/JWuWiLqbqkg9vozYk5SaWBqV4QtkCGcU6qFjEaPGK4UNif2l8IajWVkKJ8aRm5t6nKPdSDdugbz2Cjqt8jl7uZ0tLu9Uc0jc90P7CXq7wjY33WrlaGe6vaN/VZvMbBqffwwGDbyf38PDw8PDw8PDwsBHsyo/lA4t1s5zEwYqSh4z5LIjfJ2LuinE0uHJVcX7FsIYl2xOulSYONbXzsMJNxMRXgdzxqhBz+w53X7K2dFJVp1Cfyc1MVZ7IOtoKMWGihJCUmGPG1sGkaB3EvggC85hcpCYh0EsnShmplmqwOmChBIgLdtrBc8D91AQm9bmqcjkdkPxHsa2qVTJsWRWteTFS4CALl1pCVIXqfpesyKL0HF0DxX9GsSySJZTGMDWettS+qLGX6vEjJBzJklwxUWpQTSYbIAyUK99jH4bWGzCAyLR1WVl230S2G7KcOzuB1AFZJWmo1qzbriLpvq3vQhxrOuEOEuFavVQdqhxiTAkk+UExpkRW4+QupMGfJjXaF9105PIK3zuxbCW5vHOURUxuJhhwiXARIVbKSpqxxJFaIchpI5IwUW1AjkXFvmhDCyanEcL9o7jeciAIcviE2E4lsFFc7Uo7km0TY/2wneq2jpLRTpDKmYrSUDSWROlHf0ty4bcjfE9EGlViiiS0f5OpsCS1KknVW7yzuhA8gMiXQEz7Ki/lUYwRS0yzZYmiso65GndyqR4NQvSgY0plzEnbtArmA7F8uqNjmsZgT013LgbtiISSZTVS9IeanECDadgqh/JOZLmDCZw0K0FZAYGDOlkRteeD+ypyRlHiVQlqFrlIVjHek+6dqB/qnFcVpleF7lWoJJRA779KnMJQn6sqeRRF3ke1cEkktId2YfR3tnYUySM10UdRICCoz1CO4RUtpkHp9wRjlgFoMe3eF9DmHmjkS9Ax9cQ0GkYsMY3n8pboRh6gGBJWg9rV4g6kRFbJikqGqkoQ028Da2hFLrQNkp8IRC5JVorE+dG9j9siuJVUjcLK0LY2UcCfRgjFVW4Wjfyp21RLpXIsMWM+Vu0KliMZpH0pqUe1DomhEeh+D1vDZZkpMQlNfTayFqVYZ75UwXZ0C4uEm4iOajHr729dOZ66eE3CdamLYYJqRZXfiRItkJS5js9LvE8ENTRCXdSoBJZCvooaDD5F8a78gcfgP/XBQjxmQbcJqqzDncBIGipT7g50ZB2lao4UY5qFc1SlxCzHEHIwCFNJ0hiIPpFlFS2muAAXtRg7IPNddTWFzxGFXFKso0ik0PUMaghytjkSImFQV+MfVYLcARMO9aOfSSiHNwhWErq/tI20aFGzVpTQIeAzpLAVsuhr7n3H/a5KCPV3pjadF0CV5AgxJbZRJVy0jlStuWr8J21T4zNL1lmFsVTVUyVEUS8Q9W5lkJxVN7IaS4ihPgcQnpgOPHxWvoeHh4eHh4eHx5DAiLWYZpPxXgM8yWLKJUndfd9HJeApXwcMC2RZDRsDu6AfFHdKbnvahtZRskDQShCto+7qHQPd6byY0RqOsRQ1MTH+U3TvkhGFYiJVi6F6XsVNrZbVhJKXmL1OiUOqJURtR9ZR1a0etraqYRYUi0zhCGrMJoFc42QdpXaqa1hx5UcpU6oiQmxnDCSzybIa9t7Qo87Bx5mgmEhAHhIn4zTYRdEKLbXKE+0rWqnlEAW5UhdsU135UUqXqhb9AUIuiDmVF5V9PErHiCWmmdYuK+s2QXdkIPkn5w6aFHdK2J2GwU98V3d1UcZ98cfaEY45NbPKMs0dT658QgADBA7qYkJEkKXKTyW6wYiE7oZAelVgXoxFxXoUlCFO5VHdPeVSqJhMFAaRZiKh0C6S672/k46qKnpvg2Eb0E5UB0CoE6nqou/FbVmAQnSJXJKbNUKsJy9qKZlSJIQQT09fRfi89M2RDF4OyojRGJYgCS1xbMZmUYiZUkmLQgXUUrbUThH1N4smb6WGX/SmODAESpL65KeBx4glpp2ZpOXT+y6/K+0OdGVQgSkO8YRJIIRk9eyC75K2UeUnp2/QD5ogcCKRMyvddlQNCutKUpxckqRBRG3T9hbqYjGQhIraniK5itFgTVZJ0LGkGu0Ys4oTglCSFEA6oZxtDzsjkRTLdJIMFh1PfWbhdnQs0gklqASuVCknM6wpjt4BmsAVrcwoyUVq6Up4d/Jk9STpNgCRRBpPnMW0SBrV42cDUlZxn5eqShIT41iJYON4olT5SrtlpS0LSVLUN9maqVaDcjf1e2WqQYSPMR14jFhiGs7KJ4F93A9IIxNYd19VVorc9GEdUwKRULIs4L5UF1mdEdTyezkYOLEzgpuSGH0UgqSW+BSF3ftfpqj3WvGUWU+WWyThVJKT5LcIdO9IU1bVgKX+ha28ZAmibepzSIuhDGThgQTDIEeWarH2vFQ/nbwU5MqHw4vkktpF8cAQyCobPh6NTeqxsnmt/KiuXgKk1rQFEd8nmHecZ02hTaKaQxSLo0ok6R3GstLat1OkRzsEiKvXMR14jFhimuzKW7LbijAL4uRdbe62ZNL9UDo63XaJUSRi7/aDXniyhrrHcgc5tD7AhBODWKwA3GxkMaUBPK4KR4NeVizlum0Dqi4VflNJQkXNyqdMbXL5UnwinAOtHihd5B4P3epKxj3E5sVoP7pWNa6VyCVBzXwnOSt8FhR4HXo+NGkSWSOyqko5UWyfaB1FoAtVJJglVsPB0BN5Xwpl0SxhJVtHYZsceiSQ3J5ABDYBhQhIQo/OqwLVQEo9XBQpL1l3Fd5NGovVogu9SWFFqT7WT8jn+25TGAJ1Ad7TGPwADg8PDw8PDw8PDw8bwRbTcIxpEir1pKtcC0cWxO/JikqoIUMILOgpxjQZsqJ2gsW0FUyyVaBFiaVGwZpBYQBoMXW2GFtR8lTLnGIMhZhVsiBRfXI125qseYQI1lG0DpKlkraFrYiyZqma9e5uYisqXAPdu1FgHSFLDT0zjCAIWbTI+qJaUcn1SJYZeA9RWYLc9uXV7vFQF5L6Qs9RsBgDKE6UXfmamY68KKqrnYp4KCCvj5rUROMa9YPacXgTgRJd1bETxt3QvhibKr6vXGCgn4Xz1UIEZB3tTRdW1Yg9gPAxpgOPEUtMU7uzVtbtpW8Z4050JA3VCbXtSXSfXsw3QP/4A5CA3AKhAeGs/DBR7Qm5gJJrpF0tDpngGLMqijPHyg9y23XsctuRe7/t3d7POQpuphonqJJLlUiSG5ziXYnAKX4glNQSE73IVU5lyahvBLpWvIYICUvh6yUXfRoSk1TgAkZzs8cwTk7M1C81OUUM61TJYGfOHZziWKYYMt+BrnGSZGlQ41qpH115lwzRddHxOPnJBZJakehju9AmktlykiHNeHGlFgRQJc8IUdQAenv/h0BWfs5KIKYHpCcjByOWmGbTCbPUvgGFkp8oAz/bBQNYTgtqVzVL28EqG95WDpql9PHgahsGNdViEKeKLmr9cKpMAgNisOstt104C3U3ZOmrAynGLYklLsHqi8SMyCoRPSK/FO8atkrSPSdgvCq8KKTtSUldVCGJrC3qZFVqXByRUNWKTASRTqnGjhJoolcnZiqtGfpkiXCqRIpA1kZVvSObd63eWZFM0jlKTaYi0kyg4xOBjXJP+jVJTI1DVROFaPyjsZneTdWyOozg5aIGHiOWmHamEpbvJhFFclGxDipJ6g446ZQ2ICiVJs3MKkB+KpwQRWfkEADQxISBRB2EO/NuYlI67loqYzRI0uAHg2Qs47pBg/aQxZQGV1UQmgiC6non7ILrilKSVNUKVdqgmx0sy3SfCCr5J6gSMgqZzIlkGF2ekMBEGfOq3qMqnE/tyNoKCCfnkKWNQnSIwKr6xGSpU8kf9YVkmkx0bzu7Sb3gMYz6gdqmpLEagXCrCD+LeCAuQlUXvaqBqr7DNK7j8UTvQPd9k96VPxIxJIjp8uXL7Y477rDm5mY74YQTbNmyZTZ9+nRse99999n3vvc9e+GFF8zMbOrUqXbLLbf02L4njNrZaWUd+y4/gEk9PwZc2UD+3n3XJTA1KdeK0Arf2GjgLzlw5b8vU7wzCey3Z92+paHWMLn303HXipSDQSOTHO1si+XBUkMTbqbK3UYEg2KRwsSkbbvbhqBa7qiSFEkZkXUULaFilacUxZ0KoQbkAleJn5rhrWa5q5WEDnRlIlHUm0JFEGocq9pfdLW6mxTpImwTQVqH9qVxIorFkGNbe695rwr4q+54jjt1rxXjWCEmPg6kloD6qTHwGIWeNRUOwCpXRIZU9z7q6cL4p8ZJq0UBesMQSG/3xHTgMeg2+Icfftjmz59vCxcutGeffdZOOOEEmzlzpv3lL3/B9hs2bLBzzjnHfvazn1lTU5NNnDjRPvGJT9gbb7wxwD338PDw8PDw8PDoTwy6xfQb3/iGXXzxxXbhhReamdmKFSvs8ccft/vvv98WLFjgtH/ooYeK/v2d73zHfvjDH9r69evt/PPPL7kfHeXurShPuqvttl1uu1GV7kqQNEtJYJ9iUUYl3Y0tncWr/Amj3BU+6Z9S3HwyTuUytSUexk7Balstv4qrd3QXtfbeRhVYJ5AFkkI01BU8ufKx4hJYVmnf8HWQdYxKso6G7HAC3U+CmkxBcZzq86G4w1C8ZyCW96SM+UgxcWRZilCjnKyIpcaPkkVOddur1kzUQBazKbkyHY1Z4ewfrTQwjWt5sITSmEvWVj1TXyzdDCEKqt5rGDm4J3R8TJwiqIL9atwpoZRqUF5gf0RiUIlpZ2enbdq0ya655prCtng8bjNmzLCmpibpGG1tbdbV1WVjxozBv3d0dFhHN0malpY9STP5RMzy3TKPSGC/Zbt7e0ZXuR9hKuUOQu3k3Qaypr7w4fhRkovKQ9JUZZkW6J9OaAkhNJCyhAyJXQNU2ZuQCy3Iudn8/V4uD0kYDMJqPClBdcmHQWSoXHTRYW1rkaxR18QynQi1bnuYmKklWSl2NEpNeXFfrjOvVRdSpIaIDKmufCKIeQO3fQSHGl+XOwIkYApySDiQYUrgpPh3giLR1BNYasq9BopZxdAIuMfhc6ikGcsP06hL346amKgmP6nhQr2N2UMgsconPw08BpWYvv3225bL5ay2trZoe21trb300kvSMa6++mqrr6+3GTNm4N8XL15sN910k7M9n4xbrhsZLW91B422cpc0JCExqbPTHSRqK912LZDRPz6jWiqL/52DEqU1YOGlD4QsCwQil2SVoeB8PAdKHFEiElh+wtnw6rHUgQ3IukyuaBCmjHalopFZ6YOxas2ke6dWdMEMYZHop0SrLJwjFp6Io9SKVydSzPLX4kTRUglWT0xOIgtcaF+UgQOollC2yGpEms8LMatEfgVdVCRhImlWSzITVL1TltASy6gKC/0oclT0bVKpaVkvQI3Zp7HjPQofYzrwGHRXfhTceuuttnr1atuwYYNlMjzpXXPNNTZ//vzCv1taWmzixImW7MhZWbfBeGeNS0JHVYIbCKShSGCfXsyDUpA4BQvQ+lGUxFS8LQsW05YudwAbhWRV25aMuYMQWQIQ9GGqgxUMdLFEyJWLbmGR5MkWQ5EgqtYBNUkGEwxC50A5IiohKLqe1WdDUkayTE1p1vG/btz/v/twfH7+pPKglZ8kYqLKBbEep4swgaHvUK0L399AGSRxccWkqzRZpRxJuamvBBFElfyiy790DdTwoiOKWD+VPJU9V6q4vay2EWHcGUTk8zHUNO9tH4/SMajEdOzYsZZIJGzbtm1F27dt22Z1dXX73XfJkiV266232pNPPmkf+tCHemyXTqctDe7SbFncYmX7t5jubHfdgFT5addOd5KYUOUOHH/Z7b6sH6ggUWggnaFM/TEZkK2KawRBJZdkHaUYM7KOYiZpFJdvaAAjjcmgY6e7n2p9VGNWVQscQa3WomSD48AP51SzyNW+oTVTIJI9bVNJYhhyvXtNJJ9jAl2oLnQiaxw7CtXlSFYpfPwIllC0UkawhKrGIbm6VOjeoTUXtlHGvFohitzgqtQWga28dI7erciqPi2/m1rfZCLZm7zT/iAvfoeWXFQ+F+tBq3z/+3iUjkEN4EilUjZ16lRbv359YVs+n7f169dbY2Njj/vdfvvtdvPNN9vatWtt2rRpJZ27oqWj6NeVSji/eDxwfl1dceeXKc85v905c34VycD57XUTdP9l8zHnV1GWL/q1Z+POryPv/nJB3vkF5v5yQdb5EWLwXzbf6fzyQc75BbGY8+OTxN1fprLoFwQ552fxpPujY1G7fN79JVPaL8i7v7KM+6N95T6HfomU+1OvlW55osz9lZU7Pxl0XrpPBOV+0vHpntOx4BePJZxf3nLuj95r+C8bdDo/+u7oeDGLO78gCIp+dHz6hqlveA1BXvvBf3Re+d7BOZT7K18X9rcLfu410FhH7wm1U89B/VPGXPoPPyX6L3B//A3DuKN+w7ms+1PGNTrHIGOvxbSvv1KwfPlymzRpkmUyGWtoaLBf/epX+23/yCOP2FFHHWWZTMaOP/54e+KJJ4r+HgSB3XjjjTZhwgQrLy+3GTNm2O9///uiNu+8846de+65VlVVZTU1NXbRRRfZrl3FORz/8z//Y6eddpplMhmbOHGi3X777SVdn4pBjyyeP3++3Xffffbggw/a5s2b7dJLL7XW1tZClv75559flBx122232Q033GD333+/TZo0yZqbm625udm5kR4eHh4eHh4e7wX0VTrzmWeesXPOOccuuugie+6552zWrFk2a9asgsa72R4j3l133WUrVqywjRs3WkVFhc2cOdN2795nvT733HPtd7/7na1bt84ee+wxe/rpp+2SSy4p/L2lpcU+8YlP2CGHHGKbNm2yO+64wxYtWmT33nvvAbsXsaDH5dLA4e677y4I7E+ZMsXuuusua2hoMDOzj3zkIzZp0iRbuXKlmZlNmjTJ/vjHPzrHWLhwoS1atKjXc7W0tFh1dbWdee5KK+vmWt5V47pP3x3vup4PP3qHs41C+yZNcN0WdRA7WgNekHGQEFUeimOlKk81adfKWQOKAZQQVQVudnL5UNwpufLJXVZGFUxoRYxSSLt6byOuroMsyCqpcVJispYsg6VWFwpbOksVoTc3XrfHfkRxvasxoOq+YZc8hVmoFZjgnGSpUlzqZj0lNWmC7QSUfRLc4BRj2v9JUu6+qjQcjQlKNnwUOSpV8omr4WmRbhgaIMbJUvhBqedU+6sqEOArR+MEhvyIYQDk3u/WrqWlzaon/LPt2LHDqqqgQMsBxF6ucPajD1iqQlOt2YvO1jZb/Y8X9qnfDQ0NduKJJ9rdd99tZnu8xxMnTrTLLrsMpTNnz55tra2t9thjjxW2nXTSSTZlyhRbsWKFBUFg9fX1dsUVV9iVV15pZmY7duyw2tpaW7lypZ199tm2efNmO+aYY+zXv/51wfu8du1aO+OMM+xPf/qT1dfX2z333GPXXXedNTc3Wyq1Z95ZsGCBrVmzRk5S7yuGRPLTvHnzbN68efi3DRs2FP37tdde65dz5pIJi3fLku5KuR9r1Rh3oO/scAewmjEu0YmSldcB8Snjyos/ajp+WQwmV3ApjMLEYm0gRekSCrqn41G8nzr4hYmYqJ3nZPNbD/GpXSA1Q1WpKHs9QUlHbjtyhQdhfdaezhEidU6WupkeJ0aIEmMrxnHKULRNxSpSFC6Sh/hnNY5PJZzcDhKnILZV6Yuqp8naoUTMKDGLCAwldWmON7onXfB9JuPF581CZTkiw0S4ZBIG6MmtHgbH8WrjJJ5XSHRTk+vw+mnMpeFf1jalTH2xFCqhu6VnCFR+ipL8tFeeci96ynkpRTqzqampKLHbzGzmzJm2Zs0aMzPbsmWLNTc3FykWVVdXW0NDgzU1NdnZZ59tTU1NVlNTUxQSOWPGDIvH47Zx40b7h3/4B2tqarK//du/LZDSvee57bbb7N1337WDDjpIvCs6hgQxHQyEdUwp+endVrd0Ye0Ety4wvbSjib/Au01C/GQN3dVVPCBUgmxVR94dNKpSMEG6p8SBLgWWUHWgV0sSovaeEmCP+nc0MdPhKXs3iralKL9F5xUtv5hgpBxLFLCPRGrVvqglTonohPelCUuUbaJt2by7uCQrmqpooZJQtX+KxbSTyACASB0thqlgByFHsnK4ICaLJj2L4vuUgOegZqp35twFZwKtedpzTcX7EGctgJQElLKv6vUjiNQTaVatngT8PktIiFSVNg4ggnzfk5+Cv77/EydOLNrek2e3FOnM5uZmbN/c3Fz4+95t+2szfvz4or8nk0kbM2ZMUZtDDz3UOcbev3li2o8I65i+A257ehWzWXdrptwdmLeDt7gC7nYdjHNJmBDUSSIM1bJAE19HziXhsQRl5bsrQJbBAcJF7n1C2EKqus+pQhC69zTrAFYXotAAIGFo5YS67ZLUjFqfmkBkMC26qsj1hlZkkYSKSg1hokeTN7myVXKp1oBXobrQS824JislETha+JEFkhbD5LYn/WTWStbMcqRA4p7TPVYi5t5fGutUzWYCjWFqeEdZDLwyMMYq2ft0zqRp16VqwHJBFLihqtseOwPfGI0Jw8hi+vrrrxe58sla6uFixBLTiu27rSy170NpBRNntgqqksDKKQ6DsOrK304eD5BkSoTc9DQIp+Gc4VKmZmaVZaBZSq5ncVIna1NZ3HUNowYkyfQoEkeqvBEhR6t0ICtJWDVgLCpZPUhMX5tMYoouKloaRAklkhVTBfYVKauejidbaol0AvkXkCcrNYBIrS6SrloWSw8hCPeFFlLU35w4r7eDDF4aQlSiVLRpI05D3AfIr7sjHYsst1AQBBemEGMLx2NtZ/cbo2ethgGEyTTq3wqFCXo6PhorwGKKY7PqtlctnVjYotu9GwIW0yjEtKqqSooxLUU6s66ubr/t9/7vtm3bbMKECUVtpkyZUmgTTq7KZrP2zjvvFB2HztP9HP2NwX/qHh4eHh4eHh4jFKVIZzY2Nha1NzNbt25dof2hhx5qdXV1RW1aWlps48aNhTaNjY22fft227RpU6HNU089Zfl8vpCA3tjYaE8//bR1datmuG7dOjvyyCMPiBvfbARbTHceVG7J9D6LWK7M5ehkCY2Dy4tE9/Mpd/WaASMabaNYVCXzNUX9hVU/ueMImEVMcXxgCaVtCViVo1UKrXyhbdS3DpAMo2NBGABaOGm1TrckM9rdSNbGUsX0CWp2PCacwb6q1SNKLKqYDa9mr4dBVlWqY97fmepcDaq0pKae2uWcMp3ufuQWJ0uoavXsyFEykduOLKsUs8og0XlhN7jWsFfJrCeLLFkRS0+cUkNI1Kz5cP+U6lB7jg9hRkJsck/tMO601IIYZnqJ4yHnyu97N0rp9vz5823OnDk2bdo0mz59ui1dutSRzjz44INt8eLFZmb25S9/2U4//XS788477cwzz7TVq1fbb37zm4KMUywWs8svv9y+9rWv2eTJk+3QQw+1G264werr623WrFlmZnb00UfbJz/5Sbv44ottxYoV1tXVZfPmzbOzzz7b6uvrzczsC1/4gt1000120UUX2dVXX20vvPCCfetb37JvfvObfb9IESOWmKbbuqwsu+/jbq1yXRS720HeBL6jzg73A66pdElIK8SnViS1MIDwZBKWj+oLaGLCKk9kUIea8hTH1ZFzs81jwLhxQMRyJWG5ICBSlKlNIwRmdKtSQ+4muaa6ShKVetQUm6nKO6lSU3hdWnwaZb6rUFySnFnu9q0L3le1Ao9KONUqT1yZh75/InrF16bEZvYFtFiluPYOOG9HXiNw9OmMgnEs7MqnxTVdP4VAUewsoSxeenUlQhIX4Zh2CtuK9+VkOLh+A4UDCD2gd5PasVBLBAk9Gnd6CxeCJN+BxkBVfpo9e7a99dZbduONNxakM9euXVtINNq6davFu42/J598sq1atcquv/56u/baa23y5Mm2Zs0aO+644wptrrrqKmttbbVLLrnEtm/fbqeeeqqtXbu2qIT7Qw89ZPPmzbOPf/zjFo/H7bOf/azdddddhb9XV1fbT3/6U5s7d65NnTrVxo4dazfeeGOR1ml/Y0jomA4k9mqTfXLO/yvSMd0+1o0nbKtxA5XrDnatcge9z7XUVI92B4mD3ek+MzoAADQgSURBVDwXex8EhlKS1Pjy4oGTiClNJKRjShYOGsBRixQSnVQdUxr8yBog6QKqg6GqndnpJnohVG3TKNqeRAjDRFTVcVUlusRxNEqZRvV4StJJFDKoWjOJ6FIWNS3qVOsoESzF2hgmqmb8/asElqyNqmclCujNCV8ZjVd4LOivuoCne0fjGk2XPIZp3gEaT8MgAwHH8LvXilZU+F7peKyYAh1EhRRRn5nQbRHe0tJm1bVnD6qO6ZkPPGRlo/qmY9rV1maPX3juoPR7OGDEWkzDyIM1L54FApcGSRKwmCaqyNVCE4fWv/AEUwljRgVaH3o/lplZhq4/giQJWaoSkNFOYOttcf8oWYtX6aIlVM3KxlW/SAhVbU+Q1ZEGdSVpysxyYM0MIrjM5Ax5TP5w+0Lud+W9o/dG1Y5UCTe698liLH53RKYolKcNXPJhkDVTSiQys3aR6KqZ+irKgXSGz0sknN5WOpYaUkDXSu8wET1ClBCVMBHlhRQtwjTjAn6v8O0kgB7g9xRF0aI3reR+VssoBVGSnzxKw4glpun2LivL7T/WrGq8Jg2SKXc/LrJ68gDuDiajwL0fthqQuz9LUi5wxqqkO9AlRFkVGtRU6RoCD6a9ExOUNyHJI3WFT6ROzfxXKw5FIcShdij+LYZoqLI1qmWRJjB2eYIFkmS6AErcHYH60SVm+KvOpE4M2tQmJvqOiYiFQURqOyhwkCdEtaISqeOYTdoGYUugBkJvSZgkJ+HFpmIidE7qG90TisUla6sqpk+SXGSBLZX8qrGjaugJVaCicR0z9XOifiqBxt3uBgZVaeUAIpeLWbyPrvlcCa58j30Y/OWIh4eHh4eHh4eHh41gi2m48lMApoBdO90VbnWNZm0h766aDbujk6qmxEL/JlF/12IyNuOuOMlikohpK9N0Qou1QU1FSDAhy5eiPYjxT2oJTTWzHPVUwTqahQB+agcgC0QMVQ56dwN2wX0jkNW7E4opcClEVT9Te9aqtmP4PtHx1WQlgqpjyt+Oe17F9W5m1potrWRmFiwyaqiAqrGsVqqjc1DCErVTrLJk4C0XLcGYwAXnrIAkGzpeGm4KehHEMqJc7KT3b12O6yYLN4w5CXP7S+M1JkklKXsf+kLjJI3Z3cfiKNXn+gn5oARX/gDEZw9nDP5THyRkkwmLdfugEjDiJiAunV7QbBdM9DBuZGAwJbkoQniQpEGTqq1Q4gDHmLrnxPrZciUd7cMkiR+qJCUBBjGKp8Sa0uQ8UAd/cuWLMZt4P2HiCIOIJN03mlxiWBqWaqprMmBYqQnaqVVzSpVaUuvHK3JMZtHc1uSO5xjz0iYwcj2r2esEvC6xa/o9Ka0vdKwkPBwqg6pWzKN9mVxr5DKKJFX426G4Tg6pKl0GkLapFQJjqsUFq9X1EvIUIbGyvxCUEGMa+BjTSBixxDSZzVmym0RIEhKddrW5H1IqrX0oKeA5u9HKATGWQBLDE0yUCZIG6wAtd25HKKkpHXflBnBgFgPnFY2+KNnhWJKUiDRNOGhtAFIHx6N91RiwcDsioUTy2YpIcW0Q1yv2V9VZVOPiCMqzVYkvxY7Sd0ILPZVwqfHe6pweJqJEmolcqdZRgmqBpHOQtZXusbJw5sSk0gk3xfpT2VOOEy49thO/E7D8h+M9UXeXwuThG6ZEQtW4oFpzUQOaQBJ/vUnoiQmdBxI++WngMfhPfZDQlUqapfZdfnuF68oYVSnWu8bVttuOBlhVYD8csE+TQRWI+pNOIO1Lge5qnWUCli41d2BKxVyZLq7lXHwdqP4nSrSQ21atqa1meatAzU5h8usi1zskMATwHFQrIltR6X3SjqcmulG78EKE+kEJJ2VwT9rBfUhkpQM8IVgr3tnChKgd3PasWdz7pKaL5GuWW7ouarcL7gmNdaxP6raj44XHP0pWouuiZKVdcM+51KpGQmkRQp4wLsRA21yE3eo0NuEYiYtQ8BiRtRUNBJq3iTfBOcgtTzqm3TEUBPYHSMfUYx9GLDENIw+j6+527fZQVn4rhM4cDJyG2kERKmdAHAVdI+JLsW5s9XAn9d1dLjGvLANLHcn0iG4rypAmS5VzLJEg6pYAbaan4Ua13qoZ6ITwBKNaPdQqR2gdFWNWcQEjiv1zbCe9n8XXoS6QdnVptcI5FlOzhPJ56f3XiA5bG4s3qkRSlXxSySqPHaXFjvZ0vHC7ViCvRELpnNSO3hyOJ+29bz2BF9eaVqoStoKxniLYS6OFBsTF7H3WQKUXr5c5ltyHAwxvMR14jFhimk0nzFL7f+mpJCmBXkJy5VPlJ5oQaigUJzT400BKyU9jIPmJJibVzYYB8eLgSgNdqQOsmlxAUJOwyM2Wj2lEFysOiYLtmBDmCMxrFm61rCZBtaLS4kJ9/nrcafG/yfWazVPfKHyGdBzp3dSIJJ1DTf4hKLGYURKuVBBBzhJZwfvk9kX13oSvvxwSPdUwA7rnWTgnaaAqC4Q9oNh+beGoLJzR+xAjwq2ROKmACfSjp3YEDuUSyVp3uaistrA+kNhTkrSvxPQAdWaEwMtFeXh4eHh4eHh4DAmMWItpGDnwn5entdUaWVbJHU9WDhLiL4eFb2VInJqsFOh6cg+F7ig6HrUjS11nrt3ZppTaMyu9Wk88IOugtkylhACsVBXBKkvnwAx50SUftnzmIF6XohH4WKUrBnTBs44Cep/oPoVd4+HvwYyteVHQChZINcudknNYzsndVxHYV8MCaByic3IyVa/d+Ou+mhWVoAjbk0RVWkgQNdPLtFKWfw48XGT1JUSp9B3+PmV3v5irpVaIUmK99+wLCVY484j3xFd+GvEYscQ0m4wXjdrpdveDS44FdwkMdBSL2lXhDhzkyq8q07Jcw+4sdim6+ymVVfbsS8dz964s01z5XOLOHfxScTf5CTM/QwMdy5u4z0HNVKda0bQvDcxqRRfMtofYTmVSo+vHuE4AJzrAIkxMnCKoIQSq9mYY5LZWCJ2ZWSfWp6d74u6ryjRhGU1xXlbIL90jtbyxmkWvklo+L12DWva0dwUS6i+BYn1xUSPK7+nyXu6+KSK/QlgRVoeCWE/VlR9FVkqVlaPFCr4TtEjuXvY5OQRc+T75acAxYolpPh4rSnjqKIdVXzvEJwG5rBxN8kbuOSsgVkqVcwnHj9Kgqcqq0L40WNMgHCWoX4md3HOO3hMnmPhqupuUqU4klM5BMbE0cahxrGr2utImD/Gq6r45KP2nxqwSdsOLrRIdglsHHr5NlEvS2tG7rsaT0rfOWflaRvtuMIapRKzUY7GsknYOOp66r0KwqW+qlBd5keidoHj6dCRi6iIjJvKoiX1hqOMV6xNrigEpMSdAHSdiqG3abSwaAjqmXmB/4DFiiWlqd9bKun0AZR0wgrvynLgSope2HQ7Hg5pGVsNWGQoLoImUBmEyem3vdF+FiqQmRK66mrhCCskFuVBEp3MGrnIY5Ll+vCZ5RJJXNAir18/EuXfB+n5P9BInALpPKuGi91/dV9GtVC2m1A+SLSJE0Q/mvmjbwq8xqXmo5DUKkcR2Io/a1QWLevIYhcYxIpeqjimBQx40CzQt/onU0jusJo6GvzHSkyaoBTES/SwDSOMJgT1hvVhRI4RE9Be8K3/gMWKJ6c6D0pZM73PfklxURbk7kGTK4cOEwYoGfxqsSMeU4k7DE11Z3O0H6ZiqMVZjIJ6WSENCdBfTIMxue3IDuTclTGCJmHYFrtUTq6aIriyOfxQrSSH502SlopTWdPumimlrx1Nd2dSOrFwqgQ1vU/cjtIvqGGxFLd1FT6SOLJqEMBFV3dt0/CgufwJ7h9xtbDHv/ZnR8yIySNJQBCK1tDBBLVY4HrmtiUwTJGkoIq9ipSYCKYFQwQ61XDB6grBgCyUt9FIyWqwgeCARlODKD7wrPxIGP7LYw8PDw8PDw8PDw0awxTTVkbdkt1Vdfqy76uvs1FagyTJNn4+sHOSSw0x9wRrAotZapipZgirgujqxGxAnCudVBeYxFjUUx0lZpBRPpeqJUpyoGhOqWmCxTKeYJBRux6EH7jnJskjWTIJqMVPd1hjbWaIbnONEtb6p1lE1ToyskvSdkHeE7rHipqdrpX6QlZJA/aVxSAVdA+kz74Sw6PB9Uq+hFRKdMHYUrJlqGAS9d9kIFlP6/hOhCkmqnjBBD58iC6w7xhLU4iRxHKCg4RDMyo95V/6AYsQS0zDoRRpVAQLr0K5tl/sBjxmrCYeXOiCmgTTqFW3cduSOVSuuEHhAJEkSImG9u7exJKUY1xkTS2OW6j7vaV8KKyDBfiWsSpUe4so62sKE2qnkkt47tW65UvOdFmF0DbwIKz3GVE0mom1EwlQyGW5HbYisdgJBpOIfpRLkvqAFrl/pSw4/Q21sUkNUCPRukhA/AcOgIMGUE9GK26lV1AKU/Cv9BmBMLCVTQV9orM/G4Hh04u7VoHqrDDUQyAd6vE73fTxKxhB46oODso5ckcU02OW+SC0QdzO6yiWcVdXuB0cTTo04gfHkV9wwGSdLECUmaR+ImtHMpfu0wVq1GCoBJqqUCcoxQQY6EkTBctlTOwLVcicoVs6knHBCVl9tX0zqEasrqSUu1Qz5ls5wjLVGmokgEIGLEk9JBI4skJipDyS5ExqG7wlVviLsxoRItx0+L3FdpsaYqgjfz4PA0oqJSRGsVJys5G7LB+77VAWEk8DVsKRd+xVqDDtJ6KnWUTXpEhH08P8HCfF8wNbe/SDwxDQSRiwx7UonLOim0kwC+9VVrhuYkp/SkHREA3MK60y77cgaUpUKC+y7bQg04RAwex+g1t5m661ao7n3hB2yDpAMFEEvtUcZ/e47IQ+42Bd3myKPo7rj0ToK5JKORnRbKZe5B5r8knq88H1Sy2+quptEJNVwHNpXtazSmEB6x+G+RLGEcda/ZuGm95WugUN+XCjWZrqXFBahQn3nKJkUk+7IshrXbgAtQtKhG8DZ7O4NUHVM5QSmCBn41E4+R/f7OQTkomK5wGLqyrXbPh6lY8QS07COaRzuBGXbd0I9+lZoN66cKmRofSMLzKjQREwZo2qmMpNaTQaFoJ6DSHIcrLwoTh+aiGnSxLhWsI5SOxXq4FpqRZ895+hdo1ElDTvBYkrtXJ1Qhpr5TpO/GnfHVs7i49G3RH1T5wciP2rGPAn2o8EEF6Zan7ty+/+3Gd8TIojkPqfjgVMGQZXqiPyq3qHwwpyk94iYqtWm1HAktZIUh63QdwJjLPQ5vDDHsCgQ2Fer6Knu+ADG5hhQBrKi0hiualsPNR3TWAkW07y3mEbC4EcWe3h4eHh4eHh4eNgItphmy2JFS/M4WHMo0amqxtXKJI2z3WD1zEGSI+U9UhiAIiitLtLIcklWnyzUo0+Di4osENhfMWGDLAtlofNSjCWF3WH8J8SJqQLzqutdtSyq1uawtTUbobKImjilWoKwFC60awWB9VIz+ttEYXqyGEbR8VSfF33/rXBPVLF7JflJTaRSS5ISqF0LPFe1yh2FwIeHYroGeq5xMYEvHK9vxrH4qog/jX/kgajEewLvhJP8RLHZokUSLKZqBj5W+RN1p+kT42Ii9FLsO14QjxCz0U+IB323mMaHQGGA9zJGLDGN58zi3UbKLGTlJmHAIVe+KrCvTgg0cYbdbzyhk2wVBdxrmdoVYriALKYukh/qizOAQyIRif+r1bb6u4RclOPRhBheOKgxweRSp76p2ev0DquJcwT1eOGMblq8tUbw+lH4DAFJI+wrC+eLrnYlZpPOSf3FTHh6XCR5Bfc9D+1a4PkTWaWFuRNPK8b6ElTCTd+EGp+vfusc3kJjdvF5y4A06nHyLki2j0uXUvInjCdwDlI5IWBYVbcXKjYECF4sH1isj8S0r+09ijFiiWk+YZbvNmplICY0ndaqPJVnqM68e06erEuLi1ItckRCabIaDfJTZJVLwECh1q2O8q2GB3W6LqrPrlo41TKYfK3uvgSsJCTqZ4b7pyZwUDuarKOU5NQrLpVeKz78PRGRVLU9CaxAAccjTwjKYGnnLVV+KoqUE+2raoWq0lgqKNGrPGQyjVIulUuNuqSuJu3eFLbwlp4kpiasJkNWQlW2jogpKZVgOWeqaEfzFRVqEsmjSlaHmo5p2Iil7nMg8c4779hll11mP/nJTywej9tnP/tZ+9a3vmWVlZU97rN792674oorbPXq1dbR0WEzZ860b3/721ZbW1tos3XrVrv00kvtZz/7mVVWVtqcOXNs8eLFlkzueXaPPvqo3XPPPfb8889bR0eHHXvssbZo0SKbOXNm4RiLFi2ym266qejcRx55pL300kvy9Y1YYppLxC3WzR9cBq789nb39lRmXRmMXAS9S5qYlJKkBJpciXCQdYySZCpABgWTNeTMau3jpgE83Gfqh5pwoOpdKqUxe9qmgkTBFfLLLmXt/rIlVLMYUTY83XeVEKru9/Dx1KxvNTGpFTQ21blIDQ1QsbMT+hx6ZlTUo323+y510EI67e6rXgO1g1tnrtAQX9coMJmGJbTI0qqWZOXS0O7OLVBMJayE0tO+9C2Se18tcRv+7jArn3SXQRovDuFYNFwlgApQgpUKItNklSW7THeXvypPdSBRilxUX9v3Feeee669+eabtm7dOuvq6rILL7zQLrnkElu1alWP+3zlK1+xxx9/3B555BGrrq62efPm2T/+4z/aL37xCzMzy+VyduaZZ1pdXZ0988wz9uabb9r5559vZWVldsstt5iZ2dNPP21/93d/Z7fccovV1NTYAw88YGeddZZt3LjRPvzhDxfOdeyxx9qTTz5Z+PdeYqtixBLTbHnSgm5+qURO8+WlYLDaDQS2qgy0MuFdpexSykKtLCvemax5vHLXLJwEebCWjqbHbCnufdWaqcYwqvGEamwnV+Fy26l15t2+udtIY1G3rJYusE9Q3coEurYwWYlSn36nVvtCrm3PahjuNgwXAoJJMevheHcac8ibQ9jZ6u6bAu8QoRWIAo1hakU7RQpKleiqcDch9FAWWsC6J6ZtfA6tQqAS765KOZHFlCyXZDElLU7SNlUlpEg1AMnqEMNQI6abN2+2tWvX2q9//WubNm2amZktW7bMzjjjDFuyZInV19c7++zYscO++93v2qpVq+xjH/uYmZk98MADdvTRR9svf/lLO+mkk+ynP/2pvfjii/bkk09abW2tTZkyxW6++Wa7+uqrbdGiRZZKpWzp0qVFx73lllvsxz/+sf3kJz8pIqbJZNLq6upKvsbBt5N7eHh4eHh4eAwztLS0FP06Otzk6b6iqanJampqCqTUzGzGjBkWj8dt48aNuM+mTZusq6vLZsyYUdh21FFH2Qc+8AFramoqHPf4448vcu3PnDnTWlpa7He/+x0eN5/P286dO23MmDFF23//+99bfX29HXbYYXbuuefa1q1b+3SNI9ZimujIWaJbtZ94JVgWKfkHXP41GXe1SVYEWpUTSBfQ6Zt4LC55Jwbhi9Y2rD1O54iQJBbuM+ukuts6ROtIu2hZVaHGrBJYA7X369c1RjXrKFlbyRKoPgtVsJ7jLosbRtEsJdC+mNGvhq2I3yclU2bBYh62tpKFk/ZT+6Zacwn0vEaDF5jucUZQF1DGQzPdSktQNXZVfWIaT5UQJTM3XIb6liIBe1FgnzLhKQmJLKEUVsCeOq0vmLDVbVMUzen+QpTkp4kTJxZtX7hwoS1atChSf5qbm238+PFF25LJpI0ZM8aam5t73CeVSllNTU3R9tra2sI+zc3NRaR079/3/o2wZMkS27Vrl33+858vbGtoaLCVK1fakUceaW+++abddNNNdtppp9kLL7xgo0ePlq5xxBLTXDph1q3yUxyilctEN1sipSUYURwf1zJ393XOKQbXq+5jtc46JrAAgS8T67HTAE4ETrknsts6QkiBKrVEUPdV3h31WlWytgskf1SZHtXlTdBjG4v7p2bHq89GFtMXVTlIao7IH4H2DZ8jSj+oHS24e+id1KqLjie+T2HVAEqQGl2mjbkqaGyisCVMiBLjTtXFb3hMxHKxUDiEquGpMaZl5pbfJtF9zsDXrgtDCAwK0QwxWhLFlf/6669bVVVVYXs67d7nvViwYIHddttt+z3u5s2b+9SPA4lVq1bZTTfdZD/+8Y+LiPKnPvWpwv//0Ic+ZA0NDXbIIYfYD37wA7voooukYw+tN2AAkSjLF1kEaNLogm000Pd3Vi4hE2KiqpQJxrpBScoasMDQYI2apUDgaBAmEkpJV4SwhViVaME4UTFxKEpSE51DJ5O991nt207KTMFzutuINKjZ4GrlHzW2t0WIC2UlDK0dJQ7Rt04gktjZSfJzoCTRDslvuCAOVQOiGGtxIZ0EUqdOvHRest7Su4OVnyi0PzQrkbyVClU7ldJ86Hsth2dIi/UK0dqsJiweaKj6pKolNBe4Aw9ZRxMghdU9flZVJDiQiOeCErLy97SvqqoqIqb7wxVXXGEXXHDBftscdthhVldXZ3/5y1+KtmezWXvnnXd6jOusq6uzzs5O2759e5HVdNu2bYV96urq7Fe/+lXRftu2bSv8rTtWr15tX/ziF+2RRx4pCg8g1NTU2BFHHGGvvPLKftt1x4glpqlUzpLdBtRRFe6H9L5xbl30inL3Q6GBswJGurAMihlbpRT3Ew1oRPzo+Oh6hwmHj0cDGH20QP6gFeqdQrsw+aVzspWWBleyGGvWbJUQqhZzehasCxruh3t8etYUUqIuhqJAXaztpnABoX+qNBQSbiCSRLhwG/SXSChbR4E4iucIu+nRlQ/nTEEGfhas46RjHgc3BRJdWKzmYaxT34lwiXpaz9GzVvVfo2ibqmFQLNOmfevhcYe8ReTi5m0gkk/JVXAOLFPai+t9L3ojnPuDSn4HCgMlsD9u3DgbN25cr+0aGxtt+/bttmnTJps6daqZmT311FOWz+etoaEB95k6daqVlZXZ+vXr7bOf/ayZmb388su2detWa2xsLBz361//uv3lL38pWEDXrVtnVVVVdswxxxSO9f3vf9/+5V/+xVavXm1nnnlmr/3dtWuX/eEPf7Dzzjuv17Z7MWKJaTweFLm0KMu1DbJXK0a5S3yyItFgUoVk1d1Gk64SA8XSI9rKnQZrsgSUQcgDxxjCICy6xhVrME0QHZCVTlBrYNP9VF3vqo4rWa8V0Xmc5MV674pOqBmTWmq3U1QgQKkh6AuRie2CxZS+YdInJld2W6v7cRLhIgunCiJ1HUCSldjuKB4e1eVPIKsv7dsBZD0OJJnGnfDzj2P1KuovLSTdVrTwVxVN1EUtkVU1LyAMHq+owIj7DlOFqLK4604mCydlzMdJpgoy+omsqqoBQ00uykqIMY0k2t0Ljj76aPvkJz9pF198sa1YscK6urps3rx5dvbZZxcy8t944w37+Mc/bt/73vds+vTpVl1dbRdddJHNnz/fxowZY1VVVXbZZZdZY2OjnXTSSWZm9olPfMKOOeYYO++88+z222+35uZmu/76623u3LmFEIRVq1bZnDlz7Fvf+pY1NDQUYk/Ly8uturrazMyuvPJKO+uss+yQQw6xP//5z7Zw4UJLJBJ2zjnnyNc4YolpZ2fC8t3MBGqpUZpwiVyqq3KShlL2JdJUCbqjBNVyoZTG3LOvljijJhgQwgkBo4A0EIhco9VTPB4nBKmxqFoykep+VvZTSSjtG662tKedFspCuqAEWtSVWjKTCBdZR4nAkhWxs8NtR+SPoBI9Ne6UriMMIuGdIPlG94mKiaDlFs5LSVdoRRWT30p13atue1V+qhzOQeNJgkIohHAcMybd4TGWYl2pXCiRS5J3Imkoiv9k0qiNk7KcFezb3eWvVrM6kBhqclFmZg899JDNmzfPPv7xjxcE9u+6667C37u6uuzll1+2tra2wrZvfvObhbbdBfb3IpFI2GOPPWaXXnqpNTY2WkVFhc2ZM8e++tWvFtrce++9ls1mbe7cuTZ37tzC9jlz5tjKlSvNzOxPf/qTnXPOOfZ///d/Nm7cODv11FPtl7/8pWQN3osRS0w9PDw8PDw8PN5rGDNmzH7F9CdNmuSoL2QyGVu+fLktX768x/0OOeQQe+KJJ3r8+4YNG3rt2+rVq3tt0xtGLDGNx4pd+aqFg6yjtFKvAbc9rcopRp6MTWFLHcUTUlWeOLjy2TWkVYOh86plL9VKQgmIlQpbIGg/AvWNhfg1C4d6XrUakmq9VOJCVUurGutJUEtyqlYptX/kag+DrI8cO+ruSy512lexXPYE9XipfO8PG7P+qSoXifWTJ0h0M5MlKEvufTgHxcXiPQn1hRxBbEHXwoLIS0XjOn3DakUn8jYlYSML9hf3mcamUcnS30MsXSpKmufy7uwURRqK41j34b2e/ORRGkYsMU2l80XJT1TRiRIHVK081OwjmQ4YD5TQAPYUqC5ld0/O3nf3JVmhSmDXbSDx0pZ1LwwnGCHBivqmThCqnuguUAyge0dEt00kcEr5TYJan5tKctKzJtc7uVRV8qsmWFEsIiFMOtU4yc4OqIYlaoeqZI3OS+ECKlQ90lLPSWMdXQOFLZCEHsXdVla5LxT1j8iqkuhXIS58CWqogLrgovAWClsigpkX5g76rlVXOSc6aS56Cg2gOFbV5U99pn2Hgvu+O6LomHqUhhFLTJMhuSgacClTn0goJYkQiISpg2SY/Kg1oGmw5gx093j0bZHFgEBklbBdtECFB+soclF0T9TSpTTh7ASyHqWcpZJdHC7RacaqD0gagTSrGc2qhZNA4vRo5RMIJuluqhZTRcDejIkplgKFvqgeGMLutt6HZTon9VcVyVeTpCgxi+JJ0RIslj0NgyyccUiGq0nTd+heA8VOU2KqqmNMYyKNnTQmUiGSMKejMYeIXxySlfIGSVKidZSsqCppVC2wQ42EEuL5vMXJzdLLPh6lY8QS086OeFHyEw2u5D4cM0orKVYB7i0isDRIHiSUD1aTlciaRyv8drBwEtSkpi7INieomarhSUK1XLB2qJaYRNjRKbq8VemiEskfXX8LEGTUiQSo/VAtoaouqOoabw8RQlpIEmmi5B8ioShYT+SXrJlwj7MdQPQoG1wlsKEHTvJOavWmlu1uVnYG1EbIskr3BAuRwAtK7n1CeFe2GGrb1CRUAldDo6TT0klIqRKtPP5pyU/oPodrla2eqIHq9i8mVqbqbkUli+pAYygmPw13jFhimkwGRWS0HDJa1RU+S/JokkS0UieSoLif6fgoq4QxS0QuNRLaChNilAx8InBKSU525WnXr8jW9NQ3akfbCFTVRhGnJ9e7KnSvypthyAdZPUWix9ZROC+5PEPvIrrjRYupXAa0XXQjABJw4wP6njrch9GV7n0Cz7e72+LlYOHdDgL+FRGsqOSiFqtQYdEB2LcMSLJzfHhfaRFGxgDaRt8OhS2NghlzF7x3aB2logi0MBcW4emEWn4UJLri7qSjljMlcLY9fItgvUXLarc+YyjCAMPHmA48Bn854uHh4eHh4eHh4WEj2GJaObrTyrotf8kSoFpMKT6TVvQUn6pWJgkvwHiFr1WbIcsqucFZx7T02E51X64uVfxvdGWD25aeDYGs3v1dklNNpiDR+bCBVNWiVa2jHWC5lWM2oR0lxKhxjGSBC8d2RhGOVys/xbNgbYIHm6ds605wg8MDysPLU9YBcYGh83aUu4NJsMvZZHn4AMhK3bbLtaKpxQmyYN9ohTCo0VVClQQAvZsUKkUgzxXtS98JWVZpbKJtURB+PGqYUVgeyIxr0ccDeP8pZwHakcuf3PsJTGqigiW079CiJbGghOSnEio/eezD0HoDBhD5fKxoMiKhY3IpZijGSvSC0OBH8aREMMLzi1qBiEgIV3SCxAlVQkYksOTeZwF8Ol4o+YVKksIzVCWaCNROjW2jCVElk0SIw+eQ41VFuSACkZBdO3uXbTLroRoQ1ZRXYzsFIXo5dhQIZxmsBohwqrFjKSCX2SQkWKkvRQjpdi0EIA79yIOL2tywUw5vgG2phHsOEuynMRafT+i9IwMBfZuqigSNCWo5X7XUMCVdYeKUezintDKNw3lwcXNlJYqn14ikKrpPCUxUDSoJFafeG8lPPsZ0oDEkiOny5cvtjjvusObmZjvhhBNs2bJlNn369B7bP/LII3bDDTfYa6+9ZpMnT7bbbrvNzjjjjD6dM1Oes7Jude9p8FMzWgkpGEzUgVPJkKfBCuY9JE1EQqmEHg24dF6saCISYrXEX7idWkWJSaO7jaBaUaNUQ6J3QtEAVTVmubb7gS+rqWbbq9uUNioJJdBEQpZLsoQSkvCA6HhZ1aQfQg4+djo+kdUk3JMcWVbhWUeRwaIE0yTF2IeeI4/D5PVxW42GRQ61GwWPgTwhtOCmeFLWcXa30dNXvENqmVK1DCiVHyWo8lPqvnGIdx0KcaXd4YnpwGPQienDDz9s8+fPtxUrVlhDQ4MtXbrUZs6caS+//LKNHz/eaf/MM8/YOeecY4sXL7ZPf/rTtmrVKps1a5Y9++yzdtxxx8nnzeeLXVo4gdNEN6r0sp80B6lu4HByEpFBIo1EEGlQ51Kj7p4kfk/naIFEB9ViqiTiqLHlJDVDUBOdaEJgS6W7jcaq/wORB6XOPIZy7HbvJdaAF7UoCSoJjVJqU3G1o5s9BxMz3HSyNhLhJPJHxyPLKrnoyTqqWj7D51D7S/1Qr4sSB9GVT2oAQELDygpmZmkoJhA2EuDYLE78nITqtsuDhZM8XKOBSKshSjj+wbibiBVfP4/hGsi9T0ZKWbNUFMmnBCs6RyzY/zgRwFw10IjnAlnTuPs+HqUjFuCbO3BoaGiwE0880e6++24zM8vn8zZx4kS77LLLbMGCBU772bNnW2trqz322GOFbSeddJJNmTLFVqxY0ev5WlparLq62j7/nystVTGqsJ2kUcpBYJ9c7xmg92SVJNcQZeUTwgOiamkkSyhre7rnVGuWU19KJZxmpVc5UmMs1TAIEqenvlHMXhQNULTUhsgaEgS452pdeFXUvRViEaNUIVJJbRkQuDASQFbJpa7GhJLVk85BRI/a9ScoxpRAEyQR3/YK97kq6gBmZpU1LuEgYkoxq+WQgR92+dN+NZWgogKvcA2EKFA4Fo3NNTTWi3GnNO4SMa0BT1057BtGJZTDSoKGGMV1kos+GXcvViWmqns/DuftrShAS0ubjRvzOduxY4dVVVU5bQ8kClzh0/daqoyCLnpGZ1e7/eCxSwal38MBg5qV39nZaZs2bbIZM2YUtsXjcZsxY4Y1NTXhPk1NTUXtzcxmzpzZY/uOjg5raWkp+nl4eHh4eHh4eAw9DKor/+2337ZcLme1tbVF22tra+2ll17CfZqbm7F9c3Mztl+8eLHddNNNzvbOXbuLdAWzsMJPVLir8gxYB3e2uavDsRlwq8Nqux1cuWlK4gnt2wZWtQxJwlEGKljMyDgSxRuBuqBwPLoOsuGHLZB0fLI0UmlQcu9xApe7jVz0rbCNrDdqIpYSx9cFcaIJcp+2usfCuvBgbSfdzTa4WNW9m6VwEaq4BNbGfMiiSVmy8Q6wZlI7DOKlMp3glXD3tEQrBTeTZRWeWZbiQsGyFPoY41DoIQ+W290pCEdqda8rlnPPmSiDMIBq93htOyCensqeghshDzqumfKQKzvvtmmBbTSGJWB8pRDrLqquBN6sUWRZBc8alVU2OEcLvLO7Q3MMVZYKwKqKCUxQ1YE0S5NxerN3QzstFpVA1lHqS/eErV079wj2DqZjN9fZZtk+xozmsiA07CFj0GNMDzSuueYamz9/fuHfb7zxhh1zzDG25oJ/G8ReeXh4eHh4eCjYuXOnVVdXD+g5U6mU1dXV2Q9/enlJ+9fV1VkqVTqRH8kYVGI6duxYSyQStm3btqLt27Zts7q6Otynrq6uT+3T6bSl0/sCjSorK+3111+30aNH286dO23ixIn2+uuv+ziQQUJLS4t/BkMA/jkMPvwzGBrwz2HwsfcZbN261WKxmNXX1w94HzKZjG3ZssU6O0vT302lUpbJuCVhPXrHoBLTVCplU6dOtfXr19usWbPMbE/y0/r1623evHm4T2Njo61fv94uv/zywrZ169ZZY2OjdM54PG7vf//7zWyfy6CqqsoPQIMM/wyGBvxzGHz4ZzA04J/D4KO6unpQn0Emk/HkchAw6K78+fPn25w5c2zatGk2ffp0W7p0qbW2ttqFF15oZmbnn3++HXzwwbZ48WIzM/vyl79sp59+ut1555125pln2urVq+03v/mN3XvvvYN5GR4eHh4eHh4eHhEx6MR09uzZ9tZbb9mNN95ozc3NNmXKFFu7dm0hwWnr1q0W76YfcvLJJ9uqVavs+uuvt2uvvdYmT55sa9as6ZOGqYeHh4eHh4eHx9DDoBNTM7N58+b16LrfsGGDs+1zn/ucfe5zn4t83nQ6bQsXLiyKQfUYWPhnMDTgn8Pgwz+DoQH/HAYf/hmMbAy6wL6Hh4eHh4eHh4eH2SAL7Ht4eHh4eHh4eHjshSemHh4eHh4eHh4eQwKemHp4eHh4eHh4eAwJDHtiunz5cps0aZJlMhlraGiwX/3qV/tt/8gjj9hRRx1lmUzGjj/+eHviiScGqKfDF315Bvfdd5+ddtppdtBBB9lBBx1kM2bM6PWZeWjo67ewF6tXr7ZYLFbQGvYoHX19Btu3b7e5c+fahAkTLJ1O2xFHHOHHpIjo6zNYunSpHXnkkVZeXm4TJ060r3zlK7Z7t1uu00PH008/bWeddZbV19dbLBazNWvW9LrPhg0b7G/+5m8snU7bBz/4QVu5cuUB76fHICEYxli9enWQSqWC+++/P/jd734XXHzxxUFNTU2wbds2bP+LX/wiSCQSwe233x68+OKLwfXXXx+UlZUFv/3tbwe458MHfX0GX/jCF4Lly5cHzz33XLB58+bgggsuCKqrq4M//elPA9zz4YW+Poe92LJlS3DwwQcHp512WvD3f//3A9PZYYq+PoOOjo5g2rRpwRlnnBH8/Oc/D7Zs2RJs2LAheP755we458MHfX0GDz30UJBOp4OHHnoo2LJlS/Df//3fwYQJE4KvfOUrA9zz4YUnnngiuO6664JHH300MLPgRz/60X7bv/rqq8GoUaOC+fPnBy+++GKwbNmyIJFIBGvXrh2YDnsMKIY1MZ0+fXowd+7cwr9zuVxQX18fLF68GNt//vOfD84888yibQ0NDcG//uu/HtB+Dmf09RmEkc1mg9GjRwcPPvjggeriiEApzyGbzQYnn3xy8J3vfCeYM2eOJ6YR0ddncM899wSHHXZY0NnZOVBdHPbo6zOYO3du8LGPfaxo2/z584NTTjnlgPZzJEEhpldddVVw7LHHFm2bPXt2MHPmzAPYM4/BwrB15Xd2dtqmTZtsxowZhW3xeNxmzJhhTU1NuE9TU1NRezOzmTNn9tjeY/8o5RmE0dbWZl1dXTZmzJgD1c1hj1Kfw1e/+lUbP368XXTRRQPRzWGNUp7Bf/3Xf1ljY6PNnTvXamtr7bjjjrNbbrnFcrncQHV7WKGUZ3DyySfbpk2bCu7+V1991Z544gk744wzBqTPHnvg5+aRhSEhsH8g8Pbbb1sulytUkNqL2tpae+mll3Cf5uZmbN/c3HzA+jmcUcozCOPqq6+2+vp6Z1Dy0FHKc/j5z39u3/3ud+35558fgB4Of5TyDF599VV76qmn7Nxzz7UnnnjCXnnlFfvSl75kXV1dtnDhwoHo9rBCKc/gC1/4gr399tt26qmnWhAEls1m7d/+7d/s2muvHYgue/wVPc3NLS0t1t7ebuXl5YPUM48DgWFrMfV47+PWW2+11atX249+9CPLZDKD3Z0Rg507d9p5551n9913n40dO3awuzNikc/nbfz48Xbvvffa1KlTbfbs2XbdddfZihUrBrtrIwYbNmywW265xb797W/bs88+a48++qg9/vjjdvPNNw921zw8hi2GrcV07NixlkgkbNu2bUXbt23bZnV1dbhPXV1dn9p77B+lPIO9WLJkid1666325JNP2oc+9KED2c1hj74+hz/84Q/22muv2VlnnVXYls/nzcwsmUzayy+/bIcffviB7fQwQynfwoQJE6ysrMwSiURh29FHH23Nzc3W2dlpqVTqgPZ5uKGUZ3DDDTfYeeedZ1/84hfNzOz444+31tZWu+SSS+y6666zeNzbdgYCPc3NVVVV3lo6DDFsv6pUKmVTp0619evXF7bl83lbv369NTY24j6NjY1F7c3M1q1b12N7j/2jlGdgZnb77bfbzTffbGvXrrVp06YNRFeHNfr6HI466ij77W9/a88//3zh95nPfMY++tGP2vPPP28TJ04cyO4PC5TyLZxyyin2yiuvFBYFZmb/+7//axMmTPCktASU8gza2toc8rl3oRD4at4DBj83jzAMdvbVgcTq1auDdDodrFy5MnjxxReDSy65JKipqQmam5uDIAiC8847L1iwYEGh/S9+8YsgmUwGS5YsCTZv3hwsXLjQy0VFRF+fwa233hqkUqngP//zP4M333yz8Nu5c+dgXcKwQF+fQxg+Kz86+voMtm7dGowePTqYN29e8PLLLwePPfZYMH78+OBrX/vaYF3Cex59fQYLFy4MRo8eHXz/+98PXn311eCnP/1pcPjhhwef//znB+sShgV27twZPPfcc8Fzzz0XmFnwjW98I3juueeCP/7xj0EQBMGCBQuC8847r9B+r1zUv//7vwebN28Oli9f7uWihjGGNTENgiBYtmxZ8IEPfCBIpVLB9OnTg1/+8peFv51++unBnDlzitr/4Ac/CI444ogglUoFxx57bPD4448PcI+HH/ryDA455JDAzJzfwoULB77jwwx9/Ra6wxPT/kFfn8EzzzwTNDQ0BOl0OjjssMOCr3/960E2mx3gXg8v9OUZdHV1BYsWLQoOP/zwIJPJBBMnTgy+9KUvBe++++7Ad3wY4Wc/+xmO83vv/Zw5c4LTTz/d2WfKlClBKpUKDjvssOCBBx4Y8H57DAxiQeD9ER4eHh4eHh4eHoOPYRtj6uHh4eHh4eHh8d6CJ6YeHh4eHh4eHh5DAp6Yenh4eHh4eHh4DAl4Yurh4eHh4eHh4TEk4Imph4eHh4eHh4fHkIAnph4eHh4eHh4eHkMCnph6eHh4eHh4eHgMCXhi6uHh4eHh4eHhMSTgiamHh4eHh4eHh8eQgCemHh4e73l85CMfscsvv3ywu+Hh4eHhERGemHp4eHh4eHh4eAwJxIIgCAa7Ex4eHh6l4oILLrAHH3ywaNuWLVts0qRJg9MhDw8PD4+S4Ymph4fHexo7duywT33qU3bcccfZV7/6VTMzGzdunCUSiUHumYeHh4dHX5Ec7A54eHh4REF1dbWlUikbNWqU1dXVDXZ3PDw8PDwiwMeYenh4eHh4eHh4DAl4Yurh4eHh4eHh4TEk4Imph4fHex6pVMpyudxgd8PDw8PDIyI8MfXw8HjPY9KkSbZx40Z77bXX7O2337Z8Pj/YXfLw8PDwKAGemHp4eLznceWVV1oikbBjjjnGxo0bZ1u3bh3sLnl4eHh4lAAvF+Xh4eHh4eHh4TEk4C2mHh4eHh4eHh4eQwKemHp4eHh4eHh4eAwJeGLq4eHh4eHh4eExJOCJqYeHh4eHh4eHx5CAJ6YeHh4eHh4eHh5DAp6Yenh4eHh4eHh4DAl4Yurh4eHh4eHh4TEk4Imph4eHh4eHh4fHkIAnph4eHh4eHh4eHkMCnph6eHh4eHh4eHgMCXhi6uHh4eHh4eHhMSTgiamHh4eHh4eHh8eQwP8P4yExDJS+33oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output = model(jnp.array(coords))\n",
    "diff = output - u(coords[:,0], coords[:,1], 0.25).reshape(-1, 1)\n",
    "resplot = np.array(diff).reshape(N_t, N_x)\n",
    "\n",
    "plt.figure(figsize=(7, 4))\n",
    "plt.pcolormesh(T, X, resplot, shading='auto', cmap='Spectral_r')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.title('Difference between approximated and analytical solution')\n",
    "plt.xlabel('t')\n",
    "\n",
    "plt.ylabel('x')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349b88bc-5bc9-44f5-b255-19e25c5384d6",
   "metadata": {},
   "source": [
    "It appears that the approximation is good, since the maximum absolute error is $\\sim 0.02$.\n",
    "\n",
    "Finally, we can see that $\\tau$ is approximated quite well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4ebf96bf-59f6-48cc-b40f-821cb1d831aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The approximated value for τ is 0.24097223579883575.\n"
     ]
    }
   ],
   "source": [
    "print(f\"The approximated value for τ is {model.tau.value[0]}.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e936047-8251-4b91-b04e-b780643a27dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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